#Instalar librerias
import importlib.util
import sys

def check_and_install_library(library_name_list: list):
    for library_name in library_name_list:
        spec = importlib.util.find_spec(library_name)
        if spec is None:
            print(f"Library '{library_name}' not found. Installing...")
            try:
                # Use pip to install the library
                # The ! prefix runs shell commands from within Jupyter
                !{sys.executable} -m pip install {library_name}
                print(f"Library '{library_name}' installed successfully.")
            except Exception as e:
                print(f"Error installing '{library_name}': {e}")
        # else:
            # print(f"Library '{library_name}' is already installed.")
    return

library_name_list = ['pandas', 'numpy', 'jupyter', 'notebook', 'yfinance', 'matplotlib.pyplot', 'json', 'ipynb', 'import_ipynb', 'datetime', 
                     'ipywidgets', 'IPython.display', 'anywidget',# widgets
                     'nbconvert', 'pandoc', 'TeX'] #To export to HTML and PDF

check_and_install_library(library_name_list)



import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

import ipywidgets as widgets
from IPython.display import display, clear_output

#from datetime import date
from datetime import datetime

import json
#import ipynb


from dataclasses import dataclass

import nbconvert

import import_ipynb
import functions # => .../functions.ipynb     file attached
importlib.reload(functions) # Reloads the module

from plotly.graph_objects import FigureWidget

%reload_ext autoreload
%autoreload 2

Library 'TeX' not found. Installing...
Requirement already satisfied: TeX in c:\users\egarcia\appdata\local\programs\python\python312\lib\site-packages (1.8)
Library 'TeX' installed successfully.

#Dates
global start_date
start_date = datetime(2021, 4, 25) # <- date in which brokerage account started
today = datetime.today()
today = today.replace(hour=0, minute=0, second=0, microsecond=0)
no_days = today - start_date
print(f" \
      Start Date: {start_date},\n \
      End Date: {today},\n \
      number of days {no_days}")

       Start Date: 2021-04-25 00:00:00,
       End Date: 2026-01-06 00:00:00,
       number of days 1717 days, 0:00:00

risk_free = 0.04152 #10-year treasury

# Open list of tickers from original Robinhood's file_df_df
file_name = "Robinhood.xlsx" # Enrique
# file_df = "App_GBM_Detalle_Portafolio_USA_1763936603841.xlsx"  # Beto
file_df = pd.read_excel(file_name)

tickers = file_df[["Ticker"]]
tickers = list(tickers["Ticker"])

no_assets = len(tickers)

print(f"Tickers: {tickers}")
print(f"Number of Assets: {no_assets}")

Tickers: ['QYLD', 'NVDA', 'PBR', 'PLTR', 'MSFT', 'AAPL', 'NU', 'DIS', 'VOOV', 'TSM', 'GLD', 'QQQM', 'VOOG', 'KO', 'SOFI', 'QSR', 'UNH', 'TSLA', 'SPYD', 'SERV', 'SOXX', 'VOO', 'OMAB', 'VYM', 'VGT', 'GOOGL', 'CME', 'GOOG', 'META', 'BAC', 'CRWD', 'NFLX', 'MCD', 'SPYG', 'VUG', 'ADBE', 'MAR', 'VTV', 'CAT', 'LMT', 'ASML', 'BRK-B', 'SPG', 'PSA', 'HD']
Number of Assets: 45

## Fundamental Analysis for all Tickers based on Financial Ratios

# Call the get_fundamental_analysis() function (...be patient takes ~71sec)
fa_df = functions.get_fundamental_analysis(tickers, start_date, today, showLogs = "no")
display(fa_df)  #Result filtered by: ['Sector','P/E Ratio','P/B Ratio']
# sys.stdout = original_stdout

ratio_criteria= {
    'CAGR_%': (">", 10.0), # %
    'P/E Ratio': ("between", (10.0, 20.0)), # ratio
    'P/B Ratio': ("<", 3.0), # ratio
    'ROIC_%': (">", 15.0), # %
    'D/E_%': ("<", 100.0), # %
    'EPS_usd': (">", 10.0), # USD. # 10% of asset's CAGR. Will automatically trigger 10% CAGR logic in the function
    'ROE Ratio': (">", 0.15), # ratio
    'EBIT Margin_%': (">", 10.0), # %
    'Gross Margin_ratio': (">", 0.40), # ratio
    'Net Margin_%': (">", 15.0), # %
    'Current Ratio': (">", 1.5), # ratio
    'Overall Risk': ("<", 5.0), # 10 is max, 0 is min.
    'Beta': ("between", (0.0, 1.0)),
    'EBITDA_usd': (">", 0.00), # USD
    'EBITDA Margins Ratio': (">", 0.15), # ratio
    'Earning Growth Ratio': ("<", 1.0), # ratio -  PEG < 1.0 (Undervalued): This is the ideal range, as it suggests the stock price is low relative to its earnings growth potential, indicating a potentially attractive investment opportunity
    'Revenue Growth Ratio': (">", 0.15), # ratio
    'Operating Margins Ratio': (">", 0.15) # ratio
}

# Plot financial Ratios
functions.plot_ratios(fa_df, ratio_criteria, plots_per_row = 3)

# Generate the data
scorecard = functions.generate_scorecard(fa_df, ratio_criteria)

# Display with a gradient highlight on the Score column
print("\n--- STOCK SELECTION SCORECARD ---")
display(scorecard.style.background_gradient(subset=['Score %'], cmap='RdYlGn'))

--- STOCK SELECTION SCORECARD ---

# Sectors and Industries
functions.print_sector_industry(fa_df)

# --- SCORE vs BETA by Sector ---
functions.plot_risk_reward(fa_df, ratio_criteria)

# --- TOP N assets in all sectors ---
top_N_data = functions.get_top_N_assets(fa_df, ratio_criteria, top_n=10)
functions.plot_sector_treemap(top_N_data)

# Grouping the top 5 to see the distribution
summary = top_N_data.groupby(['Sector', 'Industry', 'Name', 'Ticker'])[['Score %']].max().sort_values('Score %', ascending=False)
display(summary.style.background_gradient(cmap='Greens'))

# TOP N BY SECTOR AND INDUSTRY

functions.top_N_sector_industry(fa_df, ratio_criteria, n_per_sector=10)

# TOP 1 BY SECTOR

final_picks = functions.top_1_sector(fa_df, ratio_criteria)
print("\n--- FINAL BEST-IN-CLASS PORTFOLIO SELECTION ---")
display(final_picks.style.hide(axis="index").background_gradient(subset=['Score %'], cmap='RdYlGn'))

--- FINAL BEST-IN-CLASS PORTFOLIO SELECTION ---

#  Portfolio Summary - Beta and Scores

# Option A: All Assets
stats_all = functions.get_portfolio_summary(fa_df, ratio_criteria, mode="all")
display(stats_all.style.hide(axis="index"))

# Option B: Top 10 Overall
stats_top10 = functions.get_portfolio_summary(fa_df, ratio_criteria, mode="top_n", top_n=10)
display(stats_top10.style.hide(axis="index"))

# Option C: Top 1 per Sector
stats_sector = functions.get_portfolio_summary(fa_df, ratio_criteria, mode="sector_best")
display(stats_sector.style.hide(axis="index"))

--- PORTFOLIO SUMMARY: ALL TICKERS ---
Tickers Included (45): GOOG, TSM, META, OMAB, GOOGL, NVDA, BRK-B, ASML, MSFT, NFLX, ADBE, KO, MCD, AAPL, CAT, CME, PBR, MAR, PLTR, PSA, SPG, BAC, QSR, DIS, HD, NU, UNH, CRWD, LMT, VGT, SOXX, TSLA, VYM, SPYG, SOFI, SPYD, GLD, VOOV, QQQM, VOOG, VUG, VTV, VOO, QYLD, SERV

--- PORTFOLIO SUMMARY: TOP 10 OVERALL ---
Tickers Included (10): GOOG, TSM, META, OMAB, GOOGL, NVDA, BRK-B, ASML, MSFT, NFLX

--- PORTFOLIO SUMMARY: TOP 1 PER SECTOR ---
Tickers Included (10): GOOG, TSM, OMAB, BRK-B, KO, MCD, PBR, PSA, UNH, VGT

# 1. Create the widget
ticker_dropdown = widgets.Dropdown(
    options=tickers,
    #options=["KOF", "AAPL", "MSFT", "MA","NVDA", "GOOGL", "AMZN", "META", "TSM","BRK-B", "V", "JPM", "XOM", "LLY", "MRK", "UNH", "PG", "MA","CVX", "KO", "PEP", "COST", "TMO", "ORCL", "CSCO", "NKE", "VZ", "ASML", "TXN", "ABT", "TM", "SAP", "AMD", "NFLX", "NOW", "ADBE", "LVMUY", "BABA", "SHEL", "TMUS", "QCOM", "PFE", "SNY", "AZN", "TOT", "GSK", "RIO", "BHP", "MCD​"],
    #options=["BTC-USD", "ETH-USD", "USDT-USD", "XRP-USD", "LTC-USD", "ADA-USD", "DOT-USD", "BCH-USD", "XLM-USD", "LINK-USD"]
    value=tickers[0],  # Default selected value (must be from the options)
    description='Select Ticker:',
    disabled=False,
)

def on_change(selected_ticker):
    clear_output(wait=False)
    display(fa_df.loc[selected_ticker])
    
    global ticker_widget
    ticker_widget = selected_ticker

    return ticker_widget

# # 4. Link the function to the widget and capture the output
interactive_plot = widgets.interactive_output(on_change, {'selected_ticker': ticker_dropdown})

# # 5. Display the widget and the output area in your notebook cell
display(ticker_dropdown, interactive_plot)

Dropdown(description='Select Ticker:', options=('QYLD', 'NVDA', 'PBR', 'PLTR', 'MSFT', 'AAPL', 'NU', 'DIS', 'V…

Output()

# Choose any ticket from the list above
ticker = yf.Ticker(ticker_widget)
ticker_name = ticker.info.get('symbol')
print(ticker_name)

QYLD

# Example of history() of any ONE ticker for "1y"
hist = ticker.history(period="1y", auto_adjust=True)
print(ticker_name)
display(hist)

QYLD

# info
ticker_info = ticker.info
# Optional: Print in JSON format all info
#print(json.dumps(ticker_info, indent=4))

#Income Statement
ticker_income_stmt = ticker.income_stmt
# Optional: Print Income Statement
#print(ticker_income_stmt)

#Balance Sheet
ticker_balance_sheet = ticker.balance_sheet
# Optional: Print Balance Sheet
#print(ticker_balance_sheet)


# To-Do: Support the Stock selection based on Ratios using the Financial Statements

# Print info by category (some selected data only)

def print_info_by_category(info_list, name):
    print(f"\n{name}")
    for key in info_list:
        try:
            value = ticker_info.get(key, 'N/A')
            print(f"{key}: {value}")
        except Exception as e:
            print(f"Error retrieving '{key} for {ticker_name}: {e}")

# One can choose which info parameters to print in each category:
basic_info = ['symbol', 'longName', 'sector', 'industry', 'country']
market_info = ['currentPrice', 'marketCap', 'volume', '52WeekChange', 'fiftyTwoWeekHigh', 'fiftyTwoWeekLow']
financial_info = ['priceToBook', 'forwardPE', 'trailingPE', 'profitMargins', 'totalRevenue', 'debtToEquity',
                  'epsForward','ebitda','floatShares','forwardEps','grossMargins','grossProfits','operatingCashflow',
                  'operatingMargins','returnOnAssets','returnOnEquity','revenueGrowth','revenuePerShare','impliedSharesOutstanding',
                  'totalCash','totalDebt']
dividends_info = ['dividendYield','payoutRatio','dividendRate']
shares_info = ['heldPercentInsiders','heldPercentInstitutions']
technical_info = ['sharesOutstanding','beta','currency']

print_info_by_category(basic_info,      "1. Basic info:")
print_info_by_category(market_info,     "2. Market info:")
print_info_by_category(financial_info,  "3. Financial info:")
print_info_by_category(dividends_info,  "4. Dividends info:")
print_info_by_category(shares_info,     "5. Shares management info:")
print_info_by_category(technical_info,  "6. Technical info:")

# Market Cap = Current Share Price × Shares Outstanding

1. Basic info:
symbol: QYLD
longName: Global X NASDAQ 100 Covered Call ETF
sector: N/A
industry: N/A
country: N/A

2. Market info:
currentPrice: N/A
marketCap: N/A
volume: 5818312
52WeekChange: N/A
fiftyTwoWeekHigh: 18.89
fiftyTwoWeekLow: 14.475

3. Financial info:
priceToBook: N/A
forwardPE: N/A
trailingPE: 34.672573
profitMargins: N/A
totalRevenue: N/A
debtToEquity: N/A
epsForward: N/A
ebitda: N/A
floatShares: N/A
forwardEps: N/A
grossMargins: N/A
grossProfits: N/A
operatingCashflow: N/A
operatingMargins: N/A
returnOnAssets: N/A
returnOnEquity: N/A
revenueGrowth: N/A
revenuePerShare: N/A
impliedSharesOutstanding: N/A
totalCash: N/A
totalDebt: N/A

4. Dividends info:
dividendYield: 10.46
payoutRatio: N/A
dividendRate: N/A

5. Shares management info:
heldPercentInsiders: N/A
heldPercentInstitutions: N/A

6. Technical info:
sharesOutstanding: N/A
beta: N/A
currency: USD

# 7. Other info:
print("\n7. Other info:")      
other_info = {'Dividends' : ticker.dividends, 
              'Splits' : ticker.splits, 
              'Recommendations' : ticker.recommendations
            #   'Recommendations Summary' : ticker.recommendations_summary
              }

functions.print_dividends_splits_recommendations(other_info, ticker_name)

7. Other info:

HTTP Error 404:

Dividends:

Dividends:

Date
2014-01-22 00:00:00-05:00    0.257
2014-02-26 00:00:00-05:00    0.193
2014-03-26 00:00:00-04:00    0.193
2014-04-23 00:00:00-04:00    0.263
2014-05-21 00:00:00-04:00    0.245
                             ...  
2025-08-18 00:00:00-04:00    0.168
2025-09-22 00:00:00-04:00    0.170
2025-10-20 00:00:00-04:00    0.173
2025-11-24 00:00:00-05:00    0.173
2025-12-22 00:00:00-05:00    0.178
Name: Dividends, Length: 139, dtype: float64

There are not Splits in this period for 'QYLD'
There are not Recommendations in this period for 'QYLD'

import operator
from dataclasses import dataclass
from typing import Callable, Any, Optional

RetrievalFunc = Callable[[dict], Any]   #ticker.info : is a dictionary: ticker.info.get('trailingPE', 'N/A'). Look for ticker_info =  ticker.info above
CompareFunc = Callable[[Any, Any], bool]

@dataclass
class FinancialRule:
    retrieval_func: RetrievalFunc   # function used to extract the specific metric from the data source
    metric_name: str      
    target_value: Optional[Any] = None
    comparison_func: Optional[Callable[[Any, Any], bool]] = None
          # for printing pruposes
    # target_value: float             # The target value to compare against
    # comparison_func: CompareFunc    # comparison operator function (e.g., operator.lt for <)


# financial_rules = {
#     'D/E_%': FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('debtToEquity', 'N/A'), target_value=100.0, comparison_func=operator.gt, metric_name='D/E_%')
# }

financial_rules = {
    'Name':                     FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('longName', 'N/A'),            metric_name='Name'),
    'Sector':                   FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('sector', 'ETF, others'),      metric_name='Sector'),
    'Industry':                 FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('industry', 'ETF, others'),    metric_name='Industry'),
    'CAGR_%':                   FinancialRule(retrieval_func=lambda ticker_info: functions.get_cagr(ticker_info.get('symbol'), start_date, today),   target_value=10.0, comparison_func=operator.ge, metric_name='CAGR_%'),
    'P/E Ratio':                FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('trailingPE', 'N/A'),          target_value=25.0, comparison_func=operator.lt, metric_name='P/E Ratio'),
    'P/B Ratio':                FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('priceToBook', 'N/A'),         target_value=2.0, comparison_func=operator.lt, metric_name='P/B Ratio'),
    'ROIC_%':                   FinancialRule(retrieval_func=lambda ticker_info: functions.get_roic(ticker_info.get('symbol')), target_value=15.0, comparison_func=operator.gt, metric_name='ROIC_%'),
    'D/E_%':                    FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('debtToEquity', 'N/A'),        target_value=100.0, comparison_func=operator.lt, metric_name='D/E_%'),
    'EPS_usd':                  FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('epsForward', 'N/A'),          target_value=0.0, comparison_func=operator.gt, metric_name='EPS_usd'),
    'ROE Ratio':                FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('returnOnEquity', 'N/A'),      target_value=0.15, comparison_func=operator.ge, metric_name='ROE Ratio'),
    'EBIT Margin_%':            FinancialRule(retrieval_func=lambda ticker_info: functions.get_ebit_margin(ticker_info.get('symbol')),  target_value=10.0, comparison_func=operator.ge, metric_name='EBIT Margin_%'),
    'Gross Margin_ratio':       FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('grossMargins', 'N/A'),        target_value=0.40, comparison_func=operator.ge, metric_name='Gross Margin_ratio'),
    'Net Margin_%':             FinancialRule(retrieval_func=lambda ticker_info: functions.get_net_margin(ticker_info.get('symbol')),   target_value=15.0, comparison_func=operator.ge, metric_name='Net Margin_%'),
    'Current Ratio':            FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('currentRatio', 'N/A'),        target_value=1.5, comparison_func=operator.ge, metric_name='Current Ratio'),
    'Overall Risk':             FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('overallRisk', 'N/A'),         target_value=5.0, comparison_func=operator.lt, metric_name='Overall Risk'),
    'Beta':                     FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('beta', 'N/A'),                target_value=1.0, comparison_func=operator.eq, metric_name='Beta'),
    'EBITDA_usd':               FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('ebitda', 'N/A'),              target_value=0.0, comparison_func=operator.gt, metric_name='EBITDA_usd'),
    'EBITDA Margins Ratio':     FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('ebitdaMargins', 'N/A'),       target_value=0.15, comparison_func=operator.gt, metric_name='EBITDA Margins Ratio'),
    'Earning Growth Ratio':     FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('earningsGrowth', 'N/A'),      target_value=0.15, comparison_func=operator.gt, metric_name='Earning Growth Ratio'),
    'Revenue Growth Ratio':     FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('revenueGrowth', 'N/A'),       target_value=0.15, comparison_func=operator.gt, metric_name='Revenue Growth Ratio'),
    'Operating Margins Ratio':  FinancialRule(retrieval_func=lambda ticker_info: ticker_info.get('operatingMargins', 'N/A'),    target_value=0.15, comparison_func=operator.gt, metric_name='Operating Margins Ratio'),
}

def evaluate_ticker_rules(financial_rules: dict, ticker_info: dict):
    print(f"---Evaluating Rules for {ticker_info.get('symbol', 'Unknown Ticker')}---")

    if not ticker_info.get('symbol'):
        print("Status: ⚠️ SKIP (No ticker data available)")
        return       

    for rule_name, rule in financial_rules.items():
        # 1. Call the stored retrieval function to get the actual metric value
        actual_value = rule.retrieval_func(ticker_info)
        
        print(f"\nRule: {rule_name}")
        print(f"Metric: {rule.metric_name}, Value found: {actual_value}")


        if rule.comparison_func is not None:
            if actual_value is None or actual_value == 'N/A':
                print("Status: ⚠️ SKIP (Data for comparison not available)")
                continue

            # 2. Call the stored comparison function
            is_pass = rule.comparison_func(actual_value, rule.target_value)
            
            if is_pass:
                print(f"Status: ✅ PASS ({actual_value} is acceptable)")
            else:
                print(f"Status: ❌ FAIL ({actual_value} fails rule to be {rule.comparison_func.__name__} {rule.target_value})")

        else:
            print("Status: ✅ INFO (No comparison needed)")

evaluate_ticker_rules(financial_rules, ticker_info)

---Evaluating Rules for QYLD---

Rule: Name
Metric: Name, Value found: Global X NASDAQ 100 Covered Call ETF
Status: ✅ INFO (No comparison needed)

Rule: Sector
Metric: Sector, Value found: ETF, others
Status: ✅ INFO (No comparison needed)

Rule: Industry
Metric: Industry, Value found: ETF, others
Status: ✅ INFO (No comparison needed)

Rule: CAGR_%
Metric: CAGR_%, Value found: 7.28
Status: ❌ FAIL (7.28 fails rule to be ge 10.0)

Rule: P/E Ratio
Metric: P/E Ratio, Value found: 34.672573
Status: ❌ FAIL (34.672573 fails rule to be lt 25.0)

Rule: P/B Ratio
Metric: P/B Ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)
Error (get_roic): Financial data for 'QYLD' not available or incomplete.

Rule: ROIC_%
Metric: ROIC_%, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: D/E_%
Metric: D/E_%, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: EPS_usd
Metric: EPS_usd, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: ROE Ratio
Metric: ROE Ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)
(get_ebit_margin) No annual income statement found for 'QYLD'.

Rule: EBIT Margin_%
Metric: EBIT Margin_%, Value found: None
Status: ⚠️ SKIP (Data for comparison not available)

Rule: Gross Margin_ratio
Metric: Gross Margin_ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)
Error (get_net_margin): Financial data for 'QYLD' not available.

Rule: Net Margin_%
Metric: Net Margin_%, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: Current Ratio
Metric: Current Ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: Overall Risk
Metric: Overall Risk, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: Beta
Metric: Beta, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: EBITDA_usd
Metric: EBITDA_usd, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: EBITDA Margins Ratio
Metric: EBITDA Margins Ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: Earning Growth Ratio
Metric: Earning Growth Ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: Revenue Growth Ratio
Metric: Revenue Growth Ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

Rule: Operating Margins Ratio
Metric: Operating Margins Ratio, Value found: N/A
Status: ⚠️ SKIP (Data for comparison not available)

# import data from yahoo Finance
print(f"Number of days since Brokerage account was opened: {no_days}")
df = yf.download(tickers, start=start_date, end=today, auto_adjust=True)
df.info()

[***                    7%                       ]  3 of 45 completed

Number of days since Brokerage account was opened: 1717 days, 0:00:00

[*********************100%***********************]  45 of 45 completed

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1180 entries, 2021-04-26 to 2026-01-05
Columns: 225 entries, ('Close', 'AAPL') to ('Volume', 'VYM')
dtypes: float64(182), int64(43)
memory usage: 2.0 MB

prices_raw = df["Close"]
print('-- Last 5 days of prices --')
display(prices_raw.tail(5))

-- Last 5 days of prices --

# drop NaN (days without price)
prices = prices_raw.dropna()
display(prices.tail(5))

print(f"prices_raw shape: {prices_raw.shape}")
print(f"prices shape: {prices.shape}")

if len(prices) < len(prices_raw):
    print(f"Reduction of {(len(prices_raw) - len(prices))}")
    print(f"It can be noticed that the amount of rows in the dataframe dropped from {len(prices_raw)} to {len(prices)} after the drop.na() operation. This is because there are a couple of assets that are relatively new in the public market (post IPO).")

prices_raw shape: (1180, 45)
prices shape: (458, 45)
Reduction of 722
It can be noticed that the amount of rows in the dataframe dropped from 1180 to 458 after the drop.na() operation. This is because there are a couple of assets that are relatively new in the public market (post IPO).

print(f"Reminder: the original 'start_date' was: {start_date.date().strftime("%B %d, %Y")}, when the original portfolio was created.")

df_ = df['Close']
first_valid_dates = df_.apply(pd.Series.first_valid_index)
first_valid_dates.name = "First Valid Date"
first_valid_dates = first_valid_dates.sort_values(ascending=False)


print(f"\nThe asset with the smallest amount of data available is: '{first_valid_dates.index[0]}' starting from '{first_valid_dates.iloc[0].date().strftime("%B %d, %Y")}' only.")

print("\nOldest available dates for each asset:")
display(first_valid_dates)

Reminder: the original 'start_date' was: April 25, 2021, when the original portfolio was created.

The asset with the smallest amount of data available is: 'SERV' starting from 'March 08, 2024' only.

Oldest available dates for each asset:

Ticker
SERV    2024-03-08
NU      2021-12-09
AAPL    2021-04-26
SPYD    2021-04-26
PSA     2021-04-26
QQQM    2021-04-26
QSR     2021-04-26
QYLD    2021-04-26
SOFI    2021-04-26
SOXX    2021-04-26
SPG     2021-04-26
SPYG    2021-04-26
PBR     2021-04-26
TSLA    2021-04-26
TSM     2021-04-26
UNH     2021-04-26
VGT     2021-04-26
VOO     2021-04-26
VOOG    2021-04-26
VOOV    2021-04-26
VTV     2021-04-26
VUG     2021-04-26
PLTR    2021-04-26
OMAB    2021-04-26
ADBE    2021-04-26
GOOG    2021-04-26
ASML    2021-04-26
BAC     2021-04-26
BRK-B   2021-04-26
CAT     2021-04-26
CME     2021-04-26
CRWD    2021-04-26
DIS     2021-04-26
GLD     2021-04-26
GOOGL   2021-04-26
NVDA    2021-04-26
HD      2021-04-26
KO      2021-04-26
LMT     2021-04-26
MAR     2021-04-26
MCD     2021-04-26
META    2021-04-26
MSFT    2021-04-26
NFLX    2021-04-26
VYM     2021-04-26
Name: First Valid Date, dtype: datetime64[ns]

# -- PLOT ASSET PRICES --
functions.plot_prices(prices=prices, yaxis_label="Price (USD)")

# statistics about prices
prices.describe()

# Normalized Prices

prices_normalized = (prices / prices.iloc[0]) * 100


# -- PLOT ASSET PRICES --
functions.plot_prices(prices=prices_normalized, yaxis_label="Price (USD)")

# Daily Returns. Using pct_change()
# Normal (simple or arithmetic) returns
# Expresed in FRACTION. 
# If Percentage is needed then multiply by 100.
daily_returns = prices.pct_change(fill_method=None)
daily_returns = daily_returns.dropna()
daily_returns.tail(5)

results_df = functions.plot_returns_distributions(daily_returns, num_cols=4)

================================================================================
FAT-TAIL RISK RANKING (Sorted by Highest Kurtosis)
================================================================================

# -- PLOT DAILY RETURNS --
functions.plot_daily_returns(daily_returns)

# stats and summary of daily retutns
print(f"Number of days of evaluation: {(today - daily_returns.index[0]).days}")
print(f"since {daily_returns.index[0].date().strftime("%B %d, %Y")} until {today.date().strftime("%B %d, %Y")}")

max_daily_return = daily_returns.describe().loc['max'].sort_values(ascending=False)
print(f"\nThe asset with the biggest Return in a single day is '{max_daily_return.index[0]}' with {100*max_daily_return.iloc[0]:,.5}%")

min_daily_return = daily_returns.describe().loc['min'].sort_values(ascending=True)
print(f"The asset with the lowest Return in a single day is '{min_daily_return.index[0]}' with {100*min_daily_return.iloc[0]:,.5}%")

average_daily_returns = daily_returns.mean()
print(f"The average of all Daily Returns is {average_daily_returns.mean()*100:,.4}%")

max_std = daily_returns.describe().loc['std'].sort_values(ascending=False)
print(f"\nThe asset with the biggest StdDev in Daily Returns is '{max_std.index[0]}' with {100*max_std.iloc[0]:,.5}%")
print(f"The asset with the smallest StdDev in Daily Returns is '{max_std.index[-1]}' with {100*max_std.iloc[-1]:,.5}%")

Number of days of evaluation: 666
since March 11, 2024 until January 06, 2026

The asset with the biggest Return in a single day is 'SERV' with 187.07%
The asset with the lowest Return in a single day is 'SERV' with -77.647%
The average of all Daily Returns is 0.1137%

The asset with the biggest StdDev in Daily Returns is 'SERV' with 13.175%
The asset with the smallest StdDev in Daily Returns is 'VTV' with 0.83946%

# Annualized Returns
annualized_return_tickers = daily_returns.mean() * 250
annualized_return_tickers.name = "Annualized Returns:"
annualized_return_tickers = annualized_return_tickers.sort_values(ascending=False)
print("Annualized Returns (%):")
print(f"{round(annualized_return_tickers, 2)* 100}")

Annualized Returns (%):
Ticker
SERV     153.0
PLTR     123.0
SOFI      90.0
TSLA      72.0
NVDA      55.0
TSM       53.0
GOOGL     51.0
GOOG      51.0
GLD       40.0
CAT       39.0
OMAB      38.0
NU        36.0
BAC       31.0
CRWD      29.0
AAPL      29.0
NFLX      28.0
SOXX      26.0
SPYG      25.0
VOOG      25.0
VGT       24.0
VUG       23.0
ASML      22.0
QQQM      22.0
META      21.0
VOO       19.0
CME       19.0
SPG       18.0
MAR       16.0
VYM       16.0
VTV       15.0
LMT       14.0
BRK-B     13.0
QYLD      13.0
VOOV      12.0
KO        12.0
MSFT      12.0
SPYD      11.0
DIS        7.0
PBR        6.0
MCD        5.0
PSA        1.0
HD         0.0
QSR       -3.0
UNH       -8.0
ADBE     -22.0
Name: Annualized Returns:, dtype: float64

Ticker
SERV     153.0
PLTR     123.0
SOFI      90.0
TSLA      72.0
NVDA      55.0
TSM       53.0
GOOGL     51.0
GOOG      51.0
GLD       40.0
CAT       39.0
OMAB      38.0
NU        36.0
BAC       31.0
CRWD      29.0
AAPL      29.0
NFLX      28.0
SOXX      26.0
SPYG      25.0
VOOG      25.0
VGT       24.0
VUG       23.0
ASML      22.0
QQQM      22.0
META      21.0
VOO       19.0
CME       19.0
SPG       18.0
MAR       16.0
VYM       16.0
VTV       15.0
LMT       14.0
BRK-B     13.0
QYLD      13.0
VOOV      12.0
KO        12.0
MSFT      12.0
SPYD      11.0
DIS        7.0
PBR        6.0
MCD        5.0
PSA        1.0
HD         0.0
QSR       -3.0
UNH       -8.0
ADBE     -22.0
Name: Annualized Returns:, dtype: float64

# cumprod(): calculates the total return over a period by compounding the daily returns. (a)(ab)(abc)
# It reflects how an initial investment would grow if it were continuously reinvested and earned the daily returns.
cumulative_returns = (1 + daily_returns).cumprod()
cumulative_returns = (cumulative_returns - 1)*100
print("Cumulative Returns in %:")
cumulative_returns.tail(5)

Cumulative Returns in %:

# -- PLOT CUMULATIVE RETURNS --
functions.plot_cumulative_returns(cumulative_returns)

# Rendimientos Acumulados al Final del Tiempo en (%) use .prod() y -1
print(f"Final Cumulative Returns in (%) in {(today - daily_returns.index[0]).days} days of evaluation: ")
print(f"since {daily_returns.index[0].date().strftime("%B %d, %Y")} until {today.date().strftime("%B %d, %Y")}")

cumulative_returns_final = cumulative_returns.iloc[-1]
cumulative_returns_final.name = "Final Cumulative Returns (%)"
cumulative_returns_final = round(cumulative_returns_final.sort_values(ascending=False) , 1)
print("Final Cumulative Returns in %:")
display(cumulative_returns_final.all)

Final Cumulative Returns in (%) in 666 days of evaluation: 
since March 11, 2024 until January 06, 2026
Final Cumulative Returns in %:

<bound method Series.all of Ticker
PLTR     568.4
SOFI     279.8
TSLA     157.6
GOOGL    135.5
GOOG     134.6
TSM      126.3
NVDA     115.0
GLD      102.7
CAT       86.5
OMAB      79.5
BAC       66.5
NU        62.1
AAPL      57.8
NFLX      51.2
SPYG      51.1
VOOG      51.1
VGT       46.8
VUG       45.3
QQQM      42.5
CRWD      41.4
SOXX      41.3
VOO       37.9
CME       37.0
SPG       32.3
VYM       31.2
META      31.0
VTV       28.8
MAR       26.5
ASML      25.5
QYLD      24.3
LMT       23.9
BRK-B     23.7
VOOV      22.8
KO        21.1
SPYD      19.6
MSFT      17.9
MCD        6.8
DIS        5.5
PBR        3.5
PSA       -3.5
HD        -3.8
QSR      -10.2
UNH      -25.6
ADBE     -39.9
SERV     -46.7
Name: Final Cumulative Returns (%), dtype: float64>

print(f"Number of days of evaluation (period): {(today - cumulative_returns.index[0]).days}")
print(f"From {cumulative_returns.index[0].date().strftime("%B %d, %Y")} until {today.date().strftime("%B %d, %Y")}")

print(f"The asset with the best cumulated return in the Period '{cumulative_returns_final.index[0]}' with {cumulative_returns_final.iloc[0]:,.5}% in the period")
print(f"The asset with the worst cumulative return in the Period '{cumulative_returns_final.index[-1]}' with {cumulative_returns_final.iloc[-1]:,.5}% in the period")

Number of days of evaluation (period): 666
From March 11, 2024 until January 06, 2026
The asset with the best cumulated return in the Period 'PLTR' with 568.4% in the period
The asset with the worst cumulative return in the Period 'SERV' with -46.7% in the period

# Yearly volatility (risk)

# StdDev of daily returns in a 252-days year
annualized_volatility = daily_returns.std() * np.sqrt(252)

# Percentage (%)
annualized_volatility_percent = annualized_volatility * 100

# DataFrame of Annualized Volatility
volatility_df = pd.DataFrame(annualized_volatility_percent, columns=["Volatility (%)"])

print("Annualized Assets Volatility:")

volatility_df = round(volatility_df.sort_values(by="Volatility (%)", ascending=False), 2)

display(volatility_df)

Annualized Assets Volatility:

display(volatility_df.describe()) # all stocks

# summary and stats

print(f"Number of days of evaluation: {(today - cumulative_returns.index[0]).days}")
print(f"From {cumulative_returns.index[0].date().strftime("%B %d, %Y")} until {today.date().strftime("%B %d, %Y")}")

print(f"The asset with the biggest Annualized Volatility is '{volatility_df.index[0]}' with a {volatility_df.iloc[0].values[0]:,.5} %")
print(f"The asset with the lowest Annualized Volatility is '{volatility_df.index[-1]}' with a {volatility_df.iloc[-1].values[0]:,.5} %")
print(f"The Average Annualized Volatility of all assets is: {volatility_df.mean().values[0]:,.5} %")

Number of days of evaluation: 666
From March 11, 2024 until January 06, 2026
The asset with the biggest Annualized Volatility is 'SERV' with a 209.15 %
The asset with the lowest Annualized Volatility is 'VTV' with a 13.33 %
The Average Annualized Volatility of all assets is: 32.795 %

# ANNUAL DIVIDEND YIELDS
yields = functions.plot_annual_dividnd_yields(daily_returns)

Annual Dividend Yields from yf 'dividendYield' (%):

AAPL      0.39
ADBE      0.00
ASML      0.60
BAC       1.97
BRK-B     0.00
CAT       0.98
CME       1.82
CRWD      0.00
DIS       1.31
GLD       0.00
GOOG      0.26
GOOGL     0.27
HD        2.67
KO        3.00
LMT       2.70
MAR       0.86
MCD       2.48
META      0.32
MSFT      0.77
NFLX      0.00
NU        0.00
NVDA      0.02
OMAB      4.48
PBR      14.31
PLTR      0.00
PSA       4.60
QQQM      0.49
QSR       3.72
QYLD     10.46
SERV      0.00
SOFI      0.00
SOXX      0.55
SPG       4.81
SPYD      4.46
SPYG      0.53
TSLA      0.00
TSM       1.04
UNH       2.58
VGT       0.41
VOO       1.12
VOOG      0.48
VOOV      1.80
VTV       2.05
VUG       0.42
VYM       2.42
Name: dividendYield, dtype: float64

# call the original portfolio [Ticker, Current QTY]
file_df

# Close prices df
close_prices = prices.iloc[-1]
close_prices.name = "Close Price"

# Merge original df with Close Prices
weights_df = pd.merge(file_df, close_prices, on='Ticker', how='inner')

# Add column of Amount Invested for each asset
weights_df['Investment'] = weights_df["Current QTY"] * weights_df["Close Price"]

# Calculate the Total Invested
Total_invested = weights_df['Investment'].sum()
print(f"Total Invested: ${Total_invested:,.2f}")

# Add column of weights of each asset
weights_df['Weights'] = weights_df['Investment'] / Total_invested
print(f"The sum of the weights is: {weights_df['Weights'].sum()}")

weights_df.set_index('Ticker', inplace=True)
weights_df.sort_values(by='Investment', ascending=False, inplace=True)
print("Sorted by Invested amount ($):")
display(round(weights_df, 2))

Total Invested: $68,831.74
The sum of the weights is: 1.0
Sorted by Invested amount ($):

#checking the shapes of the objects to multiply
# Daily Returns Expresed in FRACTION. 
print("Portfolio daily returns = [Daily Returns] x [weights]")
print(f"daily_returns shape: {daily_returns.shape}")
print(f"weights_df shape: {weights_df['Weights'].shape}")
print(f"Matrix multiplication [{daily_returns.shape[0]} x {daily_returns.shape[1]}] x [{weights_df['Weights'].shape[0]} x 1] = [{daily_returns.shape[0]} x 1]")

Portfolio daily returns = [Daily Returns] x [weights]
daily_returns shape: (457, 45)
weights_df shape: (45,)
Matrix multiplication [457 x 45] x [45 x 1] = [457 x 1]

# option 1: Portfolio's Daily Returns: Matrix multiplication (see above)
portfolio_daily_returns = (daily_returns @ weights_df['Weights'])

# option 2: Portfolio's Daily Returns: weighted sum of the daily returns of each asset
# portfolio_daily_returns = (daily_returns * weights_df['Weights']).sum(axis=1)


portfolio_daily_returns.name = 'portfolio daily returns'
print("portfolio_daily_returns:")
display(portfolio_daily_returns) # Expresed in FRACTION (not percentage)

portfolio_daily_returns:

Date
2024-03-11   -0.009023
2024-03-12    0.027501
2024-03-13   -0.006010
2024-03-14   -0.009048
2024-03-15   -0.009670
                ...   
2025-12-29   -0.008831
2025-12-30   -0.002100
2025-12-31   -0.006179
2026-01-02    0.000196
2026-01-05    0.005278
Name: portfolio daily returns, Length: 457, dtype: float64

results_df = functions.plot_returns_distributions(pd.DataFrame(portfolio_daily_returns), num_cols=4)

================================================================================
FAT-TAIL RISK RANKING (Sorted by Highest Kurtosis)
================================================================================

# -- PLOT DAILY RETURNS --
functions.plot_daily_returns(pd.DataFrame(portfolio_daily_returns))

# Annualized Returns
annualized_return_portfolio = portfolio_daily_returns.mean() * 250
print("Annualized Returns (%):")
print(f"{round(annualized_return_portfolio, 2) * 100}%")

Annualized Returns (%):
38.0%

# Portfolio's Cumulative daily Returns
portfolio_cumulative_returns = (1 + portfolio_daily_returns).cumprod()
portfolio_cumulative_returns = (portfolio_cumulative_returns - 1) * 100
portfolio_cumulative_returns = portfolio_cumulative_returns.rename('portfolio cumulative returns')
print("Portfolio Daily Cumulative Returns:")
display(portfolio_cumulative_returns)

Portfolio Daily Cumulative Returns:

Date
2024-03-11    -0.902260
2024-03-12     1.823045
2024-03-13     1.211137
2024-03-14     0.295371
2024-03-15    -0.674509
                ...    
2025-12-29    88.416738
2025-12-30    88.021052
2025-12-31    86.859277
2026-01-02    86.895959
2026-01-05    87.882465
Name: portfolio cumulative returns, Length: 457, dtype: float64

# -- PLOT CUMULATIVE RETURNS --
functions.plot_cumulative_returns(pd.DataFrame(portfolio_cumulative_returns))

# Portfolio's final comulated return
portafolio_cumulative_final = portfolio_cumulative_returns.iloc[-1]
print(f"portafolio_cumulative Returns_final: {portafolio_cumulative_final:,.4}%")

portafolio_cumulative Returns_final: 87.88%

# Compute annualized Volatility of the Portfolio
portfolio_annualized_volatility = portfolio_daily_returns.std() * np.sqrt(252)

# Convert to percentage
portfolio_annualized_volatility_perc = portfolio_annualized_volatility * 100

print(f"Portfolio Annualized Volatility: {portfolio_annualized_volatility_perc:,.4}%")

Portfolio Annualized Volatility: 26.13%

portfolio_annualized_return = portfolio_daily_returns.mean()*252 #Annualized
portfolio_sr = round((portfolio_annualized_return - (risk_free))/portfolio_annualized_volatility, 2)
print(f"Portfolio Sharpe Ratio: {portfolio_sr}")

Portfolio Sharpe Ratio: 1.3

print(f"Number of days of evaluation: {(today - cumulative_returns.index[0]).days} days")
print(f"From {cumulative_returns.index[0].date().strftime("%B %d, %Y")} until {today.date().strftime("%B %d, %Y")}")

print(f"\nTotal Invested: ${Total_invested:,.2f}")
print(f"Portafolio_cumulative returns_final (all period): {portafolio_cumulative_final:,.4}%")
print(f"Portfolio Annualized Average Returns: {portfolio_annualized_return:,.4%}")
print(f"Portfolio Annualized Volatility: {portfolio_annualized_volatility_perc:,.4}%")
print(f"Portfolio Sharpe Ratio: {portfolio_sr}")

Number of days of evaluation: 666 days
From March 11, 2024 until January 06, 2026

Total Invested: $68,831.74
Portafolio_cumulative returns_final (all period): 87.88%
Portfolio Annualized Average Returns: 38.1843%
Portfolio Annualized Volatility: 26.13%
Portfolio Sharpe Ratio: 1.3

# Benchmark Indices
benchmark_indices = {
    "EE.UU. (S&P 500)": "^GSPC",
    "EE.UU. (NASDAQ)": "^IXIC",
    "EE.UU. (DJIA)": "^DJI",
    "EE.UU. (Russell 100)": "^RUI",
    "México (IPC)": "^MXX",
    "Japón (Nikkei 225)": "^N225",
    "Alemania (DAX)": "^GDAXI",
    "Reino Unido (FTSE 100)": "^FTSE"
}

# get data from yahoo finance
benchmarks_prices = yf.download(list(benchmark_indices.values()), start=first_valid_dates.iloc[0], end=today, auto_adjust=True)["Close"]

# Rename columns
benchmarks_prices.columns = list(benchmark_indices.keys())

print("Benchmark Indices levels:")
display(benchmarks_prices.tail(5))

[*********************100%***********************]  8 of 8 completed

Benchmark Indices levels:

# -- PLOT ASSET PRICES --
functions.plot_prices(prices=benchmarks_prices, yaxis_label="Index Level")

# Normalized Index Levels
benchmarks_prices_normalized = (benchmarks_prices / benchmarks_prices.iloc[0]) * 100

# -- PLOT ASSET PRICES --
functions.plot_prices(prices=benchmarks_prices_normalized, yaxis_label="Index Level")

# Daily Returns
daily_returns_bm = benchmarks_prices.pct_change(fill_method=None)

# limpieza básica de daily_returns (elimina la primera fila con NaN)
daily_returns_bm = daily_returns_bm.dropna()
daily_returns_bm.tail(5)

results_df = functions.plot_returns_distributions(daily_returns_bm, num_cols=4)

================================================================================
FAT-TAIL RISK RANKING (Sorted by Highest Kurtosis)
================================================================================

functions.plot_daily_returns(daily_returns_bm)

# Annualized Returns
annualized_return_bm = daily_returns_bm.mean() * 252 
annualized_return_bm.name = "Annualized Returns (%):"
annualized_return_bm = annualized_return_bm.sort_values(ascending=False)
print("Annualized Returns (%):")
print(round(annualized_return_bm, 2)*100)

Annualized Returns (%):
México (IPC)              27.0
EE.UU. (DJIA)             17.0
EE.UU. (Russell 100)      17.0
Reino Unido (FTSE 100)    16.0
Alemania (DAX)            15.0
EE.UU. (NASDAQ)           13.0
EE.UU. (S&P 500)          11.0
Japón (Nikkei 225)         8.0
Name: Annualized Returns (%):, dtype: float64

cumulative_returns_bm = (1 + daily_returns_bm).cumprod()
cumulative_returns_bm = (cumulative_returns_bm - 1) * 100
display(cumulative_returns_bm.tail(5))

functions.plot_cumulative_returns(cumulative_returns_bm)

# Final Cumulative Return in the period of evaluation for all Indices
print("Final Cumulative Returns (%)")
# cumulative_returns_final_bm = (1 + daily_returns_bm).prod() -1
cumulative_returns_final_bm = cumulative_returns_bm.iloc[-1]
cumulative_returns_final_bm = cumulative_returns_final_bm.sort_values(ascending=False)
cumulative_returns_final_bm = cumulative_returns_final_bm.rename("final cumulative returns Benchmarks (%)")
display(cumulative_returns_final_bm)

Final Cumulative Returns (%)

México (IPC)              40.065119
EE.UU. (DJIA)             25.291417
EE.UU. (Russell 100)      24.619089
Reino Unido (FTSE 100)    23.422122
EE.UU. (NASDAQ)           18.626130
Alemania (DAX)            17.265559
EE.UU. (S&P 500)          14.200866
Japón (Nikkei 225)         9.664525
Name: final cumulative returns Benchmarks (%), dtype: float64

#Annualized Volatility
annualized_volatility_bm = daily_returns_bm.std() * np.sqrt(252)

#Annualized Volatility Percentage
annualized_volatility_bm_perc = annualized_volatility_bm * 100

annualized_volatility_bm_perc.rename('Volatility Benchmarks (%)', inplace=True)

print("Volatility of Benchmark Indices (%):")
annualized_volatility_bm_perc.sort_values(ascending=False, inplace=True)
display(annualized_volatility_bm_perc)

Volatility of Benchmark Indices (%):

Alemania (DAX)            27.297079
México (IPC)              22.904904
Reino Unido (FTSE 100)    17.402071
EE.UU. (Russell 100)      17.223190
EE.UU. (DJIA)             16.435356
Japón (Nikkei 225)        15.980321
EE.UU. (S&P 500)          15.286125
EE.UU. (NASDAQ)           11.685753
Name: Volatility Benchmarks (%), dtype: float64

# Sharpe Ratios
benchmark_sr = round((annualized_return_bm - (risk_free))/annualized_volatility_bm, 2)
benchmark_sr.rename('Sharpe Ratio', inplace=True)
benchmark_sr.sort_values(ascending=False, inplace=True)
display(benchmark_sr)

México (IPC)              0.98
EE.UU. (DJIA)             0.81
EE.UU. (Russell 100)      0.75
EE.UU. (NASDAQ)           0.74
Reino Unido (FTSE 100)    0.71
EE.UU. (S&P 500)          0.42
Alemania (DAX)            0.40
Japón (Nikkei 225)        0.23
Name: Sharpe Ratio, dtype: float64

# Days of evaluation
no_days_compare = today.date() - first_valid_dates.iloc[0].date()
print(f"\nNumber of days of evaluation: {no_days_compare.days} days.")
print(f"From: {first_valid_dates.iloc[0].date().strftime("%B %d, %Y")} to: {today.date().strftime("%B %d, %Y")}")

# Summary Return and Volatility
print("\nSummary: Benchmark Indices:")
print(f"'{cumulative_returns_final_bm.index[0]}' has the largest total return {cumulative_returns_final_bm.iloc[0]:,.5}%")
print(f"and'{cumulative_returns_final_bm.index[-1]}' the lowest total return {cumulative_returns_final_bm.iloc[-1]:,.3}%")
print(f"\n'{annualized_volatility_bm_perc.index[0]}' has the largest volatility {annualized_volatility_bm_perc.iloc[0]:,.5}%")
print(f"and '{annualized_volatility_bm_perc.index[-1]}' the lowest volatility {annualized_volatility_bm_perc.iloc[-1]:,.5}%")

Number of days of evaluation: 669 days.
From: March 08, 2024 to: January 06, 2026

Summary: Benchmark Indices:
'México (IPC)' has the largest total return 40.065%
and'Japón (Nikkei 225)' the lowest total return 9.66%

'Alemania (DAX)' has the largest volatility 27.297%
and 'EE.UU. (NASDAQ)' the lowest volatility 11.686%

# Combine Daily Returns of Initial Porfolio and 
# Benchmark Indices in a single dataFrame to plot

# convert portfolio_daily_returns to datafre to merge it with Indices daily returns
portfolio_daily_returns_df = pd.DataFrame(portfolio_daily_returns)
portfolio_daily_returns_df.rename(columns={'portfolio daily returns':'Initial Portfolio'}, inplace=True)

# check if the lenghts of the dataframes match
if len(portfolio_daily_returns_df) != len(daily_returns_bm):
    print(f"Lengths of DataFrames don't match {len(portfolio_daily_returns_df)} vs {len(daily_returns_bm)} but a left merge will help")
    print("There are more dates in one dataframe than the other")


# Merge Daily Returns of Benchmark Indices and Initial Portfolio
# Note: Left-Merge on Benchmark daily returns because they don't operate on weekends or bank holidays
# thus we compare both benchmark and portfolio using the same labor day dates only.
merge_daily_returns = pd.merge(daily_returns_bm, portfolio_daily_returns_df, on='Date', how="left")
print("\nDaily Returns (merged portfolio and indices):")
display(merge_daily_returns.tail(5))

Lengths of DataFrames don't match 457 vs 354 but a left merge will help
There are more dates in one dataframe than the other

Daily Returns (merged portfolio and indices):

results_df = functions.plot_returns_distributions(merge_daily_returns, num_cols=4)

================================================================================
FAT-TAIL RISK RANKING (Sorted by Highest Kurtosis)
================================================================================

functions.plot_daily_returns(merge_daily_returns)

# Combine Comulative Returns of Initial Porfolio and Benchmark Indices in a single dataFrame to plot

# convert portfolio_cumulative_returns to datafre to merge it with Indices returns
portfolio_cumulative_returns_df = pd.DataFrame(portfolio_cumulative_returns)
portfolio_cumulative_returns_df.rename(columns={'portfolio cumulative returns':'Initial Portfolio'}, inplace=True)

# check if the lenghts of the dataframes match
if len(portfolio_cumulative_returns_df) != len(cumulative_returns_bm):
    print(f"Lengths of DataFrames don't match {len(portfolio_cumulative_returns_df)} vs {len(cumulative_returns_bm)} but a left merge will help")


# Merge Cumulative Returns of Benchmark Indices and Initial Portfolio
# Note: Left-Merge on Benchmark cumulative returns because they don't operate on weekends or bank holidays
# thus we compare both benchmark and portfolio using the same labor day dates only.
merge_cumulative_returns = pd.merge(cumulative_returns_bm, portfolio_cumulative_returns_df, on='Date', how="left")
print("\nDaily Cumulative Returns (merged portfolio and indices):")
display(merge_cumulative_returns.tail(5))

Lengths of DataFrames don't match 457 vs 354 but a left merge will help

Daily Cumulative Returns (merged portfolio and indices):

functions.plot_cumulative_returns(merge_cumulative_returns)

# Total Cumulative Returns
print("\nTotal (Final) cumulative Returns (%):")
merge_cumulative_returns_finals = merge_cumulative_returns.iloc[-1].copy()
merge_cumulative_returns_finals.rename('Final Cumulative Returns (%)', inplace=True)
merge_cumulative_returns_finals.sort_values(ascending=False, inplace=True)
display(merge_cumulative_returns_finals)

Total (Final) cumulative Returns (%):

Initial Portfolio         88.021052
México (IPC)              40.065119
EE.UU. (DJIA)             25.291417
EE.UU. (Russell 100)      24.619089
Reino Unido (FTSE 100)    23.422122
EE.UU. (NASDAQ)           18.626130
Alemania (DAX)            17.265559
EE.UU. (S&P 500)          14.200866
Japón (Nikkei 225)         9.664525
Name: Final Cumulative Returns (%), dtype: float64

# Anualized Volatility of portfolio + Benchmarks
print('\nAnnualized Volatility (%):')
merge_annualized_volatility = merge_daily_returns.std() * np.sqrt(252)
merge_annualized_volatility = merge_annualized_volatility * 100
merge_annualized_volatility.rename('Annualized Volatility (%)', inplace=True)
merge_annualized_volatility.sort_values(ascending=False, inplace=True)
display(merge_annualized_volatility)

Annualized Volatility (%):

Alemania (DAX)            27.297079
Initial Portfolio         27.296752
México (IPC)              22.904904
Reino Unido (FTSE 100)    17.402071
EE.UU. (Russell 100)      17.223190
EE.UU. (DJIA)             16.435356
Japón (Nikkei 225)        15.980321
EE.UU. (S&P 500)          15.286125
EE.UU. (NASDAQ)           11.685753
Name: Annualized Volatility (%), dtype: float64

sharpe_ratio = pd.DataFrame()
sharpe_ratio['Annualized Returns (%)'] = merge_daily_returns.mean()*252*100 #Annualized returns
sharpe_ratio = pd.concat([sharpe_ratio['Annualized Returns (%)'], merge_annualized_volatility], axis=1)
sharpe_ratio['Sharpe Ratio'] = (sharpe_ratio['Annualized Returns (%)'] - (risk_free*100)) / (sharpe_ratio['Annualized Volatility (%)'])
sharpe_ratio.sort_values(by='Sharpe Ratio', ascending=False, inplace=True)
sharpe_ratio

#fig, ax = plt.subplots()

ax = sharpe_ratio.plot.scatter(
                        x=sharpe_ratio.columns[1], 
                        y=sharpe_ratio.columns[0],
                        c=sharpe_ratio.columns[2], 
                        colormap='coolwarm', 
                        alpha=1.0,
                        figsize=(10,8))
for i, label in enumerate(round(sharpe_ratio['Sharpe Ratio'],3)):
    ax.text(sharpe_ratio['Annualized Volatility (%)'].iloc[i] + 0.05, sharpe_ratio['Annualized Returns (%)'].iloc[i], label)
    ax.text(sharpe_ratio['Annualized Volatility (%)'].iloc[i] + 0.05, sharpe_ratio['Annualized Returns (%)'].iloc[i] + 1, sharpe_ratio.index[i])

ax.figure.axes[1].set_ylabel('Sharpe Ratio level')

plt.title('Sharpe Ratio')
plt.xlabel('Risk (%)')
plt.ylabel('Return (%)')
plt.grid()
plt.show()

# Days of evaluation
no_days_compare = today.date() - first_valid_dates.iloc[0].date()
print(f"\nNumber of days of evaluation: {no_days_compare.days} days. From: {first_valid_dates.iloc[0].date().strftime("%B %d, %Y")} to: {today.date().strftime("%B %d, %Y")}")

# Summary Return, Volatility and Sharp Ratio
print("\nSummary: Benchmark Indices + Initial Portfolio")
print("\nReturns:")
print(f"'{merge_cumulative_returns_finals.index[0]}' has the largest total return {merge_cumulative_returns_finals.iloc[0]:,.5}%")
print(f"and'{merge_cumulative_returns_finals.index[-1]}' the lowest total return {merge_cumulative_returns_finals.iloc[-1]:,.3}%")
print("\nVolatility:")
print(f"'{merge_annualized_volatility.index[0]}' has the largest volatility {merge_annualized_volatility.iloc[0]:,.5}%")
print(f"and '{merge_annualized_volatility.index[-1]}' the lowest volatility {merge_annualized_volatility.iloc[-1]:,.5}%")
print("\nSharp Ratio:")
print(f"'{sharpe_ratio.index[0]}' has the best Sharpe Ratio {sharpe_ratio['Sharpe Ratio'].iloc[0]:,.4}")
print(f"and '{sharpe_ratio.index[-1]}' the worst Sharp Ratio {sharpe_ratio['Sharpe Ratio'].iloc[-1]:,.3}")

Number of days of evaluation: 669 days. From: March 08, 2024 to: January 06, 2026

Summary: Benchmark Indices + Initial Portfolio

Returns:
'Initial Portfolio' has the largest total return 88.021%
and'Japón (Nikkei 225)' the lowest total return 9.66%

Volatility:
'Alemania (DAX)' has the largest volatility 27.297%
and 'EE.UU. (NASDAQ)' the lowest volatility 11.686%

Sharp Ratio:
'Initial Portfolio' has the best Sharpe Ratio 1.617
and 'Japón (Nikkei 225)' the worst Sharp Ratio 0.231

# B&H Stocks in initial Portfolio
functions.buy_and_hold_strategy(cumulative_returns, 100000, "Portfolio Stocks")

--- Investment Analysis: Portfolio Stocks ---
From: March 11, 2024 to January 05, 2026
Period: 665 days

# B&H Indices & Portfolio (merged)
functions.buy_and_hold_strategy(merge_cumulative_returns, 100000, "Market Benchmarks Indices and Initital Portfolio")

--- Investment Analysis: Market Benchmarks Indices and Initital Portfolio ---
From: March 11, 2024 to December 30, 2025
Period: 659 days

# For individual stocks
functions.dollar_cost_averaging_strategy(prices, monthly_investment=100, title_suffix="Individual Stocks")

--- DCA Investment Analysis: Individual Stocks ---

# For Benchmarks and your Portfolio Index
functions.dollar_cost_averaging_strategy(benchmarks_prices, monthly_investment=100, title_suffix="Indices & Portfolio")

--- DCA Investment Analysis: Indices & Portfolio ---

# call the DataFrame of the Final Cumulative Returns, which is the total return of each asset in the period.
cumulative_returns_final

# MOMENTUM: Order from largest to lowest return
momentum_ranking = cumulative_returns_final.sort_values(ascending=False)
print("Momentum Ranking (Annual yield):")
display(momentum_ranking)

# Inverse Ranking
inverse_ranking = cumulative_returns_final.sort_values(ascending=True)
print("Inverse Momentum Ranking (Annual yield):")
display(inverse_ranking)


# Plot Momentum and Inverse momentum

plt.Figure(figsize=(12, 9))
momentum_ranking.plot(kind='bar', title='Momentum Ranking')
plt.ylabel('Yield (%)')
plt.grid(True)
plt.tight_layout()
plt.show()

plt.Figure(figsize=(12, 9))
inverse_ranking.plot(kind='bar', title='Inverse Ranking', color='red')
plt.ylabel('Yield (%)')
plt.grid(True)
plt.tight_layout()
plt.show()

Momentum Ranking (Annual yield):

Ticker
PLTR     568.4
SOFI     279.8
TSLA     157.6
GOOGL    135.5
GOOG     134.6
TSM      126.3
NVDA     115.0
GLD      102.7
CAT       86.5
OMAB      79.5
BAC       66.5
NU        62.1
AAPL      57.8
NFLX      51.2
SPYG      51.1
VOOG      51.1
VGT       46.8
VUG       45.3
QQQM      42.5
CRWD      41.4
SOXX      41.3
VOO       37.9
CME       37.0
SPG       32.3
VYM       31.2
META      31.0
VTV       28.8
MAR       26.5
ASML      25.5
QYLD      24.3
LMT       23.9
BRK-B     23.7
VOOV      22.8
KO        21.1
SPYD      19.6
MSFT      17.9
MCD        6.8
DIS        5.5
PBR        3.5
PSA       -3.5
HD        -3.8
QSR      -10.2
UNH      -25.6
ADBE     -39.9
SERV     -46.7
Name: Final Cumulative Returns (%), dtype: float64

Inverse Momentum Ranking (Annual yield):

Ticker
SERV     -46.7
ADBE     -39.9
UNH      -25.6
QSR      -10.2
HD        -3.8
PSA       -3.5
PBR        3.5
DIS        5.5
MCD        6.8
MSFT      17.9
SPYD      19.6
KO        21.1
VOOV      22.8
BRK-B     23.7
LMT       23.9
QYLD      24.3
ASML      25.5
MAR       26.5
VTV       28.8
META      31.0
VYM       31.2
SPG       32.3
CME       37.0
VOO       37.9
SOXX      41.3
CRWD      41.4
QQQM      42.5
VUG       45.3
VGT       46.8
SPYG      51.1
VOOG      51.1
NFLX      51.2
AAPL      57.8
NU        62.1
BAC       66.5
OMAB      79.5
CAT       86.5
GLD      102.7
NVDA     115.0
TSM      126.3
GOOG     134.6
GOOGL    135.5
TSLA     157.6
SOFI     279.8
PLTR     568.4
Name: Final Cumulative Returns (%), dtype: float64

# call the DataFrame of the Final Cumulative Returns, which is the total return of each Index in the period.
cumulative_returns_final_bm

# MOMENTUM: Order from largest to lowest return
momentum_ranking_bm = cumulative_returns_final_bm.sort_values(ascending=False)
print("Momentum Ranking (Annual yield) of Benchmark Indices:")
display(momentum_ranking_bm)

# Inverse Ranking
inverse_ranking_bm = cumulative_returns_final_bm.sort_values(ascending=True)
print("Inverse Momentum Ranking (Annual yield) of Benchmark Indices:")
display(inverse_ranking_bm)

# Plot Momentum and Inverse momentum
plt.Figure(figsize=(12, 9))
momentum_ranking_bm.plot(kind='bar', title='Momentum Ranking')
plt.ylabel('Yield (%)')
plt.grid(True)
plt.tight_layout()
plt.show()

plt.Figure(figsize=(12, 9))
inverse_ranking_bm.plot(kind='bar', title='Inverse Ranking', color='red')
plt.ylabel('Yield (%)')
plt.grid(True)
plt.tight_layout()
plt.show()

Momentum Ranking (Annual yield) of Benchmark Indices:

México (IPC)              40.065119
EE.UU. (DJIA)             25.291417
EE.UU. (Russell 100)      24.619089
Reino Unido (FTSE 100)    23.422122
EE.UU. (NASDAQ)           18.626130
Alemania (DAX)            17.265559
EE.UU. (S&P 500)          14.200866
Japón (Nikkei 225)         9.664525
Name: final cumulative returns Benchmarks (%), dtype: float64

Inverse Momentum Ranking (Annual yield) of Benchmark Indices:

Japón (Nikkei 225)         9.664525
EE.UU. (S&P 500)          14.200866
Alemania (DAX)            17.265559
EE.UU. (NASDAQ)           18.626130
Reino Unido (FTSE 100)    23.422122
EE.UU. (Russell 100)      24.619089
EE.UU. (DJIA)             25.291417
México (IPC)              40.065119
Name: final cumulative returns Benchmarks (%), dtype: float64

functions.plot_interactive_heatmap_correlation(daily_returns, triangle='half')
functions.plot_extreme_correlations(daily_returns, num_pairs=10)

================================================================================
REGRESSION & VOLATILITY SUMMARY TABLE
================================================================================

--------------------------------------------------
INTERPRETATION GUIDE:
--------------------------------------------------
• p-value Significance: Probability the correlation happened by chance. 
  Small values (e.g., < 1.42e-05) mean the relationship is mathematically solid.
• Significance?: 'Yes' if p_value < 0.05.
• Beta (Slope): The magnitude. A slope of 1.2 means for every 1% change in Stock A, 
  Stock B tends to move 1.2%.
• Alpha (Intercept): The 'excess' return of Stock B if Stock A's return was zero.
• Low Standard Error: Relationship is tight; Beta is a reliable predictor.
• High Standard Error: Lots of 'noise'; Beta is less reliable for hedging/pairs trading.
• Volatility (Vol):
  - High Vol + High Corr: High-octane comovers.
  - Low Vol + High Corr: Stable 'pairs trading' candidates.
  - Interpretation: If Vol Stock B > Vol Stock A, Beta will naturally be higher. 
    Check this to see if Beta is driven by correlation or just swing scale.

functions.plot_interactive_heatmap_correlation(merge_daily_returns, triangle='half')
functions.plot_extreme_correlations(merge_daily_returns, num_pairs=10)

================================================================================
REGRESSION & VOLATILITY SUMMARY TABLE
================================================================================

--------------------------------------------------
INTERPRETATION GUIDE:
--------------------------------------------------
• p-value Significance: Probability the correlation happened by chance. 
  Small values (e.g., < 1.42e-05) mean the relationship is mathematically solid.
• Significance?: 'Yes' if p_value < 0.05.
• Beta (Slope): The magnitude. A slope of 1.2 means for every 1% change in Stock A, 
  Stock B tends to move 1.2%.
• Alpha (Intercept): The 'excess' return of Stock B if Stock A's return was zero.
• Low Standard Error: Relationship is tight; Beta is a reliable predictor.
• High Standard Error: Lots of 'noise'; Beta is less reliable for hedging/pairs trading.
• Volatility (Vol):
  - High Vol + High Corr: High-octane comovers.
  - Low Vol + High Corr: Stable 'pairs trading' candidates.
  - Interpretation: If Vol Stock B > Vol Stock A, Beta will naturally be higher. 
    Check this to see if Beta is driven by correlation or just swing scale.

# Plot ANNUALIZED variance/covariance Matrix =  returns.cov() * 252
functions.plot_annualized_covariance_heatmap(daily_returns, triangle='half')

# Plot ANNUALIZED variance/covariance Matrix =  returns.cov() * 252
functions.plot_annualized_covariance_heatmap(merge_daily_returns, triangle='half')

# --- EFFICIENT FRONTIER & CAPITAL MARKET LINE ANALYSIS---

import functions # => .../functions.ipynb     file attached
importlib.reload(functions) # Reloads the module

functions.run_full_frontier_analysis(rets=daily_returns, 
                          curr_port_weights = weights_df['Weights'], 
                          curr_port_vol = portfolio_annualized_volatility, 
                          curr_port_ret = portfolio_annualized_return,
                          mean_ann = daily_returns.mean() * 252, 
                          cov_ann = daily_returns.cov() * 252, 
                          rf_default = risk_free,
                          long_only = True,  # Set to True for standard long-only constraints
                          portfolio_value = Total_invested, #100000
                          yields = yields/100
                          )

VBox(children=(HBox(children=(FloatText(value=4.152, description='RF Rate %:'), FloatText(value=5.0, descripti…

no_starts = 25
no_simul = 2000000

weights_optimal, vol_ret_sr_optimal = functions.efficient_froentier_sharp_ratio(
                daily_returns, 
                [portfolio_annualized_volatility, portfolio_annualized_return], 
                daily_returns.mean()*252, # Simple Arithmetic Mean Annualization
                daily_returns.cov()*252, #Annualized Covariance
                risk_free, #Risk Free
                True, # Long only = True, Short allowed = False
                no_starts, # no. of starts (25 default)
                no_simul, # no. of simulations
                123 # seed
                )

Optimizing Sharpe…

Optimum Weights (%) - Tangency Portfolio
Ticker
AAPL      0.00
ADBE      0.00
ASML      0.00
BAC       1.45
BRK-B     0.00
CAT       2.55
CME      19.14
CRWD      0.00
DIS       0.00
GLD      39.85
GOOG      0.00
GOOGL    11.69
HD        0.00
KO       12.11
LMT       0.10
MAR       0.00
MCD       0.00
META      0.00
MSFT      0.00
NFLX      0.00
NU        0.00
NVDA      0.00
OMAB      1.50
PBR       0.00
PLTR      8.47
PSA       0.00
QQQM      0.00
QSR       0.00
QYLD      0.00
SERV      0.78
SOFI      1.23
SOXX      0.00
SPG       0.00
SPYD      0.00
SPYG      0.00
TSLA      0.00
TSM       1.13
UNH       0.00
VGT       0.00
VOO       0.00
VOOG      0.00
VOOV      0.00
VTV       0.00
VUG       0.00
VYM       0.00

Optimum Sharpe Ratio: 3.040
Expected Annual Return: 42.80%
Annual Risk: 12.71%

Simulating random Portfolios…

Simulated Portfolio with Minimum Volatility:
Volatility: 11.44%
Return: 21.65%
Sharpe Ratio: 1.529

Simulated Portfolio with Maximum Return:
Volatility: 69.42%
Return: 70.52%
Sharpe Ratio: 0.956

Current Portfolio:
Volatility: 26.13%
Return: 38.18%
Sharpe Ratio: 1.302

# Recall Total Invested
Investment = Total_invested

# Current Portolio Weights and Positions
weights_df

# Optimal Weights from Efficient Frontier
weights_optimal

# Merge DataFrames
weights_df_Optimal = pd.merge(weights_df, weights_optimal, left_index=True, right_index=True, how='outer')

# New Investment (USD)
weights_df_Optimal['New Investment usd'] = weights_df_Optimal['Optimal Weights'] * Investment

# New QTY
weights_df_Optimal['New QTY'] = weights_df_Optimal['New Investment usd'] / weights_df_Optimal['Close Price']

# Difference in QTY or buy/sell position
weights_df_Optimal['Position_to_Optimal'] = weights_df_Optimal['New QTY'] - weights_df_Optimal['Current QTY']

weights_df_Optimal = round(weights_df_Optimal, 3)
display(weights_df_Optimal)

print(f"Current Investment: $ {round(Investment, 2)}")
print(f"New Investment: $ {round(weights_df_Optimal['New Investment usd'].sum(), 2)}")

Current Investment: $ 68831.74
New Investment: $ 68831.74

# Objective 1. You want a target portfolio volatility 𝜎𝑝 :

# 𝜎𝑝 (target volatility):
# 90% of the Tangency portfolio volatility (can be any value)
target_volatility = vol_ret_sr_optimal[0] * 0.90

# The fraction of total wealth X invested in the tangency (risky) portfolio
y = target_volatility / vol_ret_sr_optimal[0]

print(f"Target Volatility: {target_volatility:.2%}")
print(f"The fraction of total wealth X invested in the tangency (risky) portfolio 'y' is: {y:.2%}, that is ${y*Total_invested:,.2f}usd")
print(f"The fraction invested in the risk-free asset (1-y) is: {1-y:.2%}, that is ${(1-y)*Total_invested:,.2f}usd")
ER_p = risk_free + y*(vol_ret_sr_optimal[1] - risk_free)
print(f"Expected Return E[Rp] = {ER_p:.2%}")
print(f"Sharpe Ratio = {(ER_p - risk_free) / target_volatility:.2f} ")

Target Volatility: 11.44%
The fraction of total wealth X invested in the tangency (risky) portfolio 'y' is: 90.00%, that is $61,948.57usd
The fraction invested in the risk-free asset (1-y) is: 10.00%, that is $6,883.17usd
Expected Return E[Rp] = 38.94%
Sharpe Ratio = 3.04

# Objective 2. You want a target expected return Ep

# Ep (target Expected Return):
# 90% of the Tangency portfolio return (can be any value)
target_return = vol_ret_sr_optimal[1] * 0.90

# The fraction of total wealth X invested in the tangency (risky) portfolio
y = (target_return - risk_free) / (vol_ret_sr_optimal[1] - risk_free)

print(f"Target Expected Return: {target_return:.2%}")
print(f"The fraction of total wealth X invested in the tangency (risky) portfolio 'y' is: {y:.2%}, that is ${y*Total_invested:,.2f}usd")
print(f"The fraction invested in the risk-free asset (1-y) is: {1-y:.2%}, that is ${(1-y)*Total_invested:,.2f}usd")
sigma_p = y * vol_ret_sr_optimal[0]
print(f"Volatility: {sigma_p:.2%}")
print(f"Sharpe Ratio = {(target_return - risk_free)/sigma_p:.2f}")

Target Expected Return: 38.52%
The fraction of total wealth X invested in the tangency (risky) portfolio 'y' is: 88.93%, that is $61,209.13usd
The fraction invested in the risk-free asset (1-y) is: 11.07%, that is $7,622.61usd
Volatility: 11.31%
Sharpe Ratio = 3.04

# Objective 3. You maximize mean–variance Utility (risk aversion 𝛾)

# Gamma (risk aversion coefficient) :
# If 𝛾 is large, the investor is very risk averse — even small increases in variance are penalized heavily. 
# → They prefer portfolios with lower volatility, even if returns are modest.
# If 𝛾 is small, the investor is risk tolerant (or aggressive) — they are willing to accept more variance for more expected return.
gamma = 4

# a) Allowing borrowing (short)
# The fraction of total wealth X invested in the tangency (risky) portfolio
y_star = (vol_ret_sr_optimal[1] - risk_free) / (gamma * (vol_ret_sr_optimal[0]**2))
print("a) Allowing borrowing (short in risk asset):")
print(f"Mean-variance risk aversion coefficient: {gamma}")
print(f"The fraction of total wealth X invested in the tangency (risky) portfolio y* is: {y_star:.2%}, that is ${y_star * Total_invested:,.2f}usd")
print(f"The fraction invested in the risk-free asset (1-y*) is: {1-y:.2%}, that is ${(1-y_star) * Total_invested:,.2f}usd")
# E[R_p] = Rf + y* (E[Rt] - Rf)
ER_p = risk_free + (y_star * (vol_ret_sr_optimal[1] - risk_free))
sigma_p = y_star * vol_ret_sr_optimal[0]
print(f"Expected Return E[Rp] = {ER_p:.2%}")
print(f"Volatility sigma_p = {sigma_p:.2%}")
print(f"Sharpe Ratio = {(ER_p - risk_free)/sigma_p:.2}")

# b) For long-only, no-borrowing constraint: set 𝑦=min⁡(1,max⁡(0,𝑦∗)).
y = min(1, max(0, y_star))
print("\nb) For long-only, no-borrowing constraint:")
print(f"Mean-variance risk aversion coefficient: {gamma}")
print(f"The fraction of total wealth X invested in the tangency (risky) portfolio 'y' is: {y:.2%}, that is ${y*Total_invested:,.2f}usd")
print(f"The fraction invested in the risk-free asset (1-y) is: {1-y:.2%}, that is ${(1-y)*Total_invested:,.2f}usd")
ER_p = risk_free + (y * (vol_ret_sr_optimal[1] - risk_free))
sigma_p = y * vol_ret_sr_optimal[0]
print(f"Expected Return E[Rp] = {ER_p:.2%}")
print(f"Volatility sigma_p = {sigma_p:.2%}")
print(f"Sharpe Ratio = {(ER_p - risk_free)/sigma_p:.2}")

a) Allowing borrowing (short in risk asset):
Mean-variance risk aversion coefficient: 4
The fraction of total wealth X invested in the tangency (risky) portfolio y* is: 597.81%, that is $411,484.33usd
The fraction invested in the risk-free asset (1-y*) is: 11.07%, that is $-342,652.59usd
Expected Return E[Rp] = 235.20%
Volatility sigma_p = 76.00%
Sharpe Ratio = 3.0

b) For long-only, no-borrowing constraint:
Mean-variance risk aversion coefficient: 4
The fraction of total wealth X invested in the tangency (risky) portfolio 'y' is: 100.00%, that is $68,831.74usd
The fraction invested in the risk-free asset (1-y) is: 0.00%, that is $0.00usd
Expected Return E[Rp] = 42.80%
Volatility sigma_p = 12.71%
Sharpe Ratio = 3.0

# ANNUAL DIVIDEND YIELDS
yields_merge = functions.plot_annual_dividnd_yields(merge_daily_returns)

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Annual Dividend Yields from yf 'dividendYield' (%):

EE.UU. (S&P 500)          0
EE.UU. (NASDAQ)           0
EE.UU. (DJIA)             0
EE.UU. (Russell 100)      0
México (IPC)              0
Japón (Nikkei 225)        0
Alemania (DAX)            0
Reino Unido (FTSE 100)    0
Initial Portfolio         0
Name: dividendYield, dtype: int64

# --- EFFICIENT FRONTIER & CAPITAL MARKET LINE ANALYSIS---

import functions # => .../functions.ipynb     file attached
importlib.reload(functions) # Reloads the module

functions.run_full_frontier_analysis(rets = merge_daily_returns, 
                          curr_port_weights = weights_df['Weights'], 
                          curr_port_vol = portfolio_annualized_volatility, 
                          curr_port_ret = portfolio_annualized_return,
                          mean_ann = merge_daily_returns.mean() * 252, 
                          cov_ann = merge_daily_returns.cov() * 252, 
                          rf_default = risk_free,
                          long_only = True,  # Set to True for standard long-only constraints
                          portfolio_value = Total_invested, #100000
                          yields = yields_merge/100
                          )

VBox(children=(HBox(children=(FloatText(value=4.152, description='RF Rate %:'), FloatText(value=5.0, descripti…

weights_optimal, vol_ret_sr_optimal = functions.efficient_froentier_sharp_ratio(
                merge_daily_returns, 
                [portfolio_annualized_volatility, portfolio_annualized_return], 
                merge_daily_returns.mean()*252, # Simple Arithmetic Mean Annualization
                merge_daily_returns.cov()*252, #Annualized Covariance
                risk_free, #Risk Free
                True, # Long only = True, Short allowed = False
                no_starts, # no. of starts (25 default)
                no_simul, # no. of simulations
                123 # seed
                )

Optimizing Sharpe…

Optimum Weights (%) - Tangency Portfolio
EE.UU. (S&P 500)           0.00
EE.UU. (NASDAQ)           37.73
EE.UU. (DJIA)              7.24
EE.UU. (Russell 100)       0.00
México (IPC)               0.00
Japón (Nikkei 225)         0.00
Alemania (DAX)             1.48
Reino Unido (FTSE 100)     0.00
Initial Portfolio         53.55

Optimum Sharpe Ratio: 1.715
Expected Annual Return: 32.19%
Annual Risk: 16.35%

Simulating random Portfolios…

Simulated Portfolio with Minimum Volatility:
Volatility: 10.05%
Return: 12.42%
Sharpe Ratio: 0.823

Simulated Portfolio with Maximum Return:
Volatility: 24.61%
Return: 43.19%
Sharpe Ratio: 1.586

Current Portfolio:
Volatility: 26.13%
Return: 38.18%
Sharpe Ratio: 1.302

#recall
display(benchmark_indices)

{'EE.UU. (S&P 500)': '^GSPC',
 'EE.UU. (NASDAQ)': '^IXIC',
 'EE.UU. (DJIA)': '^DJI',
 'EE.UU. (Russell 100)': '^RUI',
 'México (IPC)': '^MXX',
 'Japón (Nikkei 225)': '^N225',
 'Alemania (DAX)': '^GDAXI',
 'Reino Unido (FTSE 100)': '^FTSE'}

# Choose a benchmark to compare for alpha, beta, R2...
index_benchmark = 0

functions.indicators(tickers, start_date, today, benchmark_indices, index_benchmark,
                     risk_free, portfolio_weights=pd.Series(weights_df['Weights']), no_starts=no_starts)

	Name	Sector	Industry	CAGR_%	P/E Ratio	P/B Ratio	ROIC_%	D/E_%	EPS_usd	ROE Ratio	...	Gross Margin_ratio	Net Margin_%	Current Ratio	Overall Risk	Beta	EBITDA_usd	EBITDA Margins Ratio	Earning Growth Ratio	Revenue Growth Ratio	Operating Margins Ratio
Ticker
NFLX	Netflix, Inc.	Communication Services	Entertainment	13.21	38.108330	14.932244	28.58	65.822	3.242550	0.42861	...	0.48085	22.34	1.332	9.0	1.711	1.296965e+10	0.29899	0.087	0.172	0.28220
GOOG	Alphabet Inc.	Communication Services	Internet Content & Information	23.97	31.355732	9.906034	30.69	11.424	11.197380	0.35450	...	0.59172	28.60	1.747	0.0	1.086	1.451740e+11	0.37661	0.353	0.159	0.30512
GOOGL	Alphabet Inc.	Communication Services	Internet Content & Information	24.10	31.247780	9.881684	30.69	11.424	11.197380	0.35450	...	0.59172	28.60	1.747	10.0	1.086	1.451740e+11	0.37661	0.353	0.159	0.30512
META	Meta Platforms, Inc.	Communication Services	Internet Content & Information	18.12	29.149998	8.557937	33.52	26.311	30.418530	0.32643	...	0.82013	37.91	1.978	10.0	1.287	9.839900e+10	0.51937	-0.826	0.262	0.40075
DIS	The Walt Disney Company	Communication Services	Entertainment	-9.24	16.628279	1.859483	7.32	39.632	7.355800	0.12203	...	0.37764	13.14	0.710	3.0	1.442	1.941900e+10	0.20566	1.873	-0.005	0.11868
TSLA	Tesla, Inc.	Consumer Cyclical	Auto Manufacturers	13.78	311.496550	18.774212	10.61	17.082	2.203830	0.06791	...	0.17007	7.30	2.066	10.0	1.835	1.076800e+10	0.11260	-0.371	0.116	0.06628
MAR	Marriott International, Inc.	Consumer Cyclical	Lodging	17.93	32.774500	-26.863880	24.47	0.000	11.383310	0.00000	...	0.81554	9.46	0.467	7.0	1.157	4.600000e+09	0.66919	0.290	0.056	0.65934
MCD	McDonald's Corporation	Consumer Cyclical	Restaurants	8.03	25.585323	-98.735590	20.02	0.000	13.229860	0.00000	...	0.57425	31.72	1.000	5.0	0.531	1.429200e+10	0.54417	0.016	0.030	0.46906
QSR	Restaurant Brands International Inc.	Consumer Cyclical	Restaurants	3.70	23.666666	6.470816	10.81	306.718	4.015010	0.25245	...	0.33528	12.15	1.059	4.0	0.605	2.701000e+09	0.29156	0.217	0.069	0.27726
HD	The Home Depot, Inc.	Consumer Cyclical	Home Improvement Retail	4.08	23.471350	28.257370	24.64	544.586	15.095210	1.62909	...	0.33355	9.28	1.051	1.0	1.072	2.558700e+10	0.15396	-0.014	0.028	0.12945
KO	The Coca-Cola Company	Consumer Defensive	Beverages - Non-Alcoholic	8.37	22.496689	9.354261	20.50	144.771	3.220510	0.42442	...	0.61633	22.59	1.211	3.0	0.387	1.630700e+10	0.34213	0.301	0.051	0.32373
SOXX	iShares Semiconductor ETF	ETF, others	ETF, others	18.82	41.787422	0.749979	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
VGT	Vanguard Information Technology Index Fund ETF...	ETF, others	ETF, others	16.07	39.488186	0.000000	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
VUG	Vanguard Growth Index Fund ETF Shares	ETF, others	ETF, others	13.41	39.276190	2.263974	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
VOOG	Vanguard S&P 500 Growth Index Fund ETF Shares	ETF, others	ETF, others	13.71	35.788900	0.000000	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
QYLD	Global X NASDAQ 100 Covered Call ETF	ETF, others	ETF, others	7.28	34.672573	0.000000	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
QQQM	Invesco NASDAQ 100 ETF	ETF, others	ETF, others	14.21	34.027912	0.000000	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
SPYG	State Street SPDR Portfolio S&P 500 Growth ETF	ETF, others	ETF, others	13.77	33.924810	1.701520	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
VOO	Vanguard S&P 500 ETF	ETF, others	ETF, others	12.82	29.083656	1.618149	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
VOOV	Vanguard S&P 500 Value Index Fund ETF Shares	ETF, others	ETF, others	10.85	23.608147	0.000000	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
VTV	Vanguard Value Index Fund ETF Shares	ETF, others	ETF, others	10.73	21.379675	2.568654	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
VYM	Vanguard High Dividend Yield Index Fund ETF Sh...	ETF, others	ETF, others	10.95	20.940632	0.000000	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
SPYD	State Street SPDR Portfolio S&P 500 High Divid...	ETF, others	ETF, others	6.48	15.968643	0.000000	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
GLD	SPDR Gold Shares	ETF, others	ETF, others	20.98	0.000000	2.404230	0.00	0.000	0.000000	0.00000	...	0.00000	0.00	0.000	0.0	0.000	0.000000e+00	0.00000	0.000	0.000	0.00000
PBR	Petróleo Brasileiro S.A. - Petrobras	Energy	Oil & Gas Integrated	36.76	5.435185	1.940786	8.81	88.498	2.536670	0.19022	...	0.48152	8.23	0.819	0.0	-0.032	1.910370e+11	0.38872	0.005	-0.013	0.36224
SOFI	SoFi Technologies, Inc.	Financial Services	Credit Services	12.71	52.285717	4.017012	0.00	31.999	0.572950	0.08593	...	0.82510	19.09	1.150	9.0	1.932	0.000000e+00	0.00000	1.059	0.378	0.15598
NU	Nu Holdings Ltd.	Financial Services	Banks - Regional	12.45	34.500004	8.236915	0.00	0.000	0.911740	0.27800	...	0.00000	23.85	0.000	0.0	1.083	0.000000e+00	0.00000	0.409	0.363	0.58215
CME	CME Group Inc.	Financial Services	Financial Data & Stock Exchanges	10.76	26.678951	3.511150	13.50	13.372	11.683760	0.13346	...	1.00000	57.52	1.021	10.0	0.291	4.494100e+09	0.70383	-0.004	-0.030	0.63386
BRK-B	Berkshire Hathaway Inc.	Financial Services	Insurance - Diversified	13.85	15.947536	0.001027	12.90	18.166	24.190810	0.10170	...	0.24361	20.98	2.722	10.0	0.710	1.038640e+11	0.27911	0.172	0.021	0.41103
BAC	Bank of America Corporation	Financial Services	Banks - Diversified	10.81	15.543715	1.499038	0.00	0.000	4.357060	0.09871	...	0.00000	26.63	0.000	4.0	1.295	0.000000e+00	0.00000	0.315	0.126	0.35293
UNH	UnitedHealth Group Incorporated	Healthcare	Healthcare Plans	-1.44	17.822823	3.234996	12.62	75.734	17.767490	0.17476	...	0.19701	3.60	0.823	8.0	0.425	2.924200e+10	0.06720	-0.602	0.122	0.03813
CAT	Caterpillar Inc.	Industrials	Farm & Heavy Construction Machinery	25.49	31.643555	13.956912	21.86	201.046	22.386720	0.46283	...	0.30120	16.65	1.384	4.0	1.568	1.395800e+10	0.21583	-0.036	0.095	0.17746
LMT	Lockheed Martin Corporation	Industrials	Aerospace & Defense	10.00	28.483854	19.035872	25.81	358.987	29.223770	0.62776	...	0.08252	7.51	1.129	5.0	0.245	7.257000e+09	0.09894	0.022	0.088	0.11693
OMAB	Grupo Aeroportuario del Centro Norte, S.A.B. d...	Industrials	Airports & Air Services	24.74	17.794788	75.197770	29.05	132.873	7.859000	0.54332	...	0.74202	32.70	1.138	0.0	0.611	9.810583e+09	0.61446	0.092	0.061	0.61165
SERV	Serve Robotics Inc.	Industrials	Specialty Industrial Machinery	-12.53	0.000000	3.024809	-355.90	1.461	-1.773330	-0.47177	...	0.00000	-2162.29	17.214	10.0	0.000	-8.331123e+07	0.00000	0.000	2.095	-50.67831
PSA	Public Storage	Real Estate	REIT - Industrial	3.31	27.092419	9.225600	12.74	106.758	10.247400	0.19933	...	0.72701	44.13	0.269	5.0	0.991	3.379439e+09	0.70476	0.213	0.031	0.46947
SPG	Simon Property Group, Inc.	Real Estate	REIT - Retail	15.45	26.653566	25.888590	13.78	884.863	6.882500	0.82455	...	0.81993	39.75	0.592	10.0	1.400	4.549947e+09	0.73919	0.274	0.082	0.50757
PLTR	Palantir Technologies Inc.	Technology	Software - Infrastructure	52.23	395.545440	62.943940	9.44	3.520	1.010140	0.19504	...	0.80808	16.13	6.427	10.0	1.545	8.757970e+08	0.22478	2.000	0.628	0.33296
NVDA	NVIDIA Corporation	Technology	Semiconductors	70.14	46.564354	38.454617	90.19	9.102	7.566360	1.07359	...	0.70050	55.85	4.468	8.0	2.314	1.126960e+11	0.60220	0.667	0.625	0.63169
ASML	ASML Holding N.V.	Technology	Semiconductor Equipment & Materials	14.83	43.291855	21.398302	79.56	14.240	30.810055	0.53852	...	0.52711	26.79	1.308	0.0	1.341	1.215790e+10	0.37743	0.038	0.007	0.32842
AAPL	Apple Inc.	Technology	Consumer Electronics	16.30	35.825737	53.548386	82.29	152.411	9.155080	1.71422	...	0.46905	26.92	0.893	1.0	1.093	1.447480e+11	0.34782	0.912	0.079	0.31647
MSFT	Microsoft Corporation	Technology	Software - Infrastructure	14.36	33.678776	9.681614	27.78	33.154	18.742380	0.32241	...	0.68764	36.15	1.401	5.0	1.073	1.664370e+11	0.56647	0.127	0.184	0.48873
TSM	Taiwan Semiconductor Manufacturing Company Lim...	Technology	Semiconductors	25.15	33.324715	52.480240	36.86	20.436	13.083090	0.34657	...	0.58976	40.02	2.693	0.0	1.274	2.484714e+12	0.68423	0.391	0.303	0.50578
ADBE	Adobe Inc.	Technology	Software - Application	-8.96	19.842012	11.895383	45.27	57.197	26.340080	0.55426	...	0.89268	25.85	0.996	1.0	1.526	9.551333e+09	0.40184	0.172	0.105	0.36503
CRWD	CrowdStrike Holdings, Inc.	Technology	Software - Infrastructure	16.29	0.000000	28.652567	-25.06	20.154	4.834480	-0.08815	...	0.74277	-0.49	1.811	10.0	1.029	-9.382400e+07	-0.02055	0.000	0.222	-0.05589

				Score %
Sector	Industry	Name	Ticker
Communication Services	Internet Content & Information	Alphabet Inc.	GOOG	83.330000
Technology	Semiconductors	Taiwan Semiconductor Manufacturing Company Limited	TSM	83.330000
Technology	Semiconductors	NVIDIA Corporation	NVDA	77.780000
Communication Services	Internet Content & Information	Alphabet Inc.	GOOGL	77.780000
Industrials	Airports & Air Services	Grupo Aeroportuario del Centro Norte, S.A.B. de C.V.	OMAB	77.780000
Communication Services	Internet Content & Information	Meta Platforms, Inc.	META	77.780000
Communication Services	Entertainment	Netflix, Inc.	NFLX	72.220000
Financial Services	Insurance - Diversified	Berkshire Hathaway Inc.	BRK-B	72.220000
Technology	Semiconductor Equipment & Materials	ASML Holding N.V.	ASML	72.220000
Technology	Software - Infrastructure	Microsoft Corporation	MSFT	72.220000

Sector	Industry	Ticker	Score %
Communication Services	Internet Content & Information	GOOG	83.330000
Technology	Semiconductors	TSM	83.330000
Industrials	Airports & Air Services	OMAB	77.780000
Financial Services	Insurance - Diversified	BRK-B	72.220000
Consumer Defensive	Beverages - Non-Alcoholic	KO	66.670000
Consumer Cyclical	Restaurants	MCD	66.670000
Energy	Oil & Gas Integrated	PBR	61.110000
Real Estate	REIT - Industrial	PSA	55.560000
Healthcare	Healthcare Plans	UNH	38.890000
ETF, others	ETF, others	VGT	33.330000

Metric	Value
Selection Strategy	ALL TICKERS
Total Tickers	45
Average Match Score	51.73%
Average Beta	0.75
Portfolio Character	CONSERVATIVE / DEFENSIVE

Metric	Value
Selection Strategy	TOP 10 OVERALL
Total Tickers	10
Average Match Score	76.67%
Average Beta	1.25
Portfolio Character	AGGRESSIVE / HIGH VOLATILITY

Metric	Value
Selection Strategy	TOP 1 PER SECTOR
Total Tickers	10
Average Match Score	63.89%
Average Beta	0.60
Portfolio Character	CONSERVATIVE / DEFENSIVE

	Open	High	Low	Close	Volume	Dividends	Stock Splits	Capital Gains
Date
2025-01-06 00:00:00-05:00	16.399330	16.434827	16.372709	16.399330	4535000	0.0	0.0	0.0
2025-01-07 00:00:00-05:00	16.443700	16.443700	16.248469	16.283966	8486600	0.0	0.0	0.0
2025-01-08 00:00:00-05:00	16.297274	16.328334	16.195223	16.310585	11652700	0.0	0.0	0.0
2025-01-10 00:00:00-05:00	16.275095	16.283969	16.062117	16.159731	15684100	0.0	0.0	0.0
2025-01-13 00:00:00-05:00	16.017742	16.133106	15.955624	16.133106	8383300	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...
2025-12-29 00:00:00-05:00	17.719999	17.750000	17.709999	17.740000	4555200	0.0	0.0	0.0
2025-12-30 00:00:00-05:00	17.730000	17.760000	17.725000	17.730000	4812700	0.0	0.0	0.0
2025-12-31 00:00:00-05:00	17.730000	17.740000	17.670000	17.670000	4961300	0.0	0.0	0.0
2026-01-02 00:00:00-05:00	17.750000	17.780001	17.620001	17.680000	10828500	0.0	0.0	0.0
2026-01-05 00:00:00-05:00	17.725000	17.789000	17.719999	17.760000	5833600	0.0	0.0	0.0

Ticker	AAPL	ADBE	ASML	BAC	BRK-B	CAT	CME	CRWD	DIS	GLD	...	TSLA	TSM	UNH	VGT	VOO	VOOG	VOOV	VTV	VUG	VYM
Date
2025-12-29	273.760010	353.160004	1066.000000	55.349998	501.049988	578.609985	278.420013	475.910004	114.190002	398.600006	...	459.640015	300.920013	328.940002	763.099976	632.599976	448.600006	206.630005	192.589996	492.540009	144.699997
2025-12-30	273.079987	352.510010	1072.140015	55.279999	503.709991	577.390015	275.829987	475.630005	114.790001	398.890015	...	454.429993	299.579987	332.160004	760.890015	631.719971	447.769989	206.410004	192.369995	491.690002	144.550003
2025-12-31	271.859985	349.989990	1069.859985	55.000000	502.649994	572.869995	273.079987	468.760010	113.769997	396.309998	...	449.720001	303.890015	330.109985	753.780029	627.130005	444.589996	204.850006	190.990005	487.859985	143.520004
2026-01-02	271.010010	333.299988	1163.780029	55.950001	496.850006	598.409973	269.679993	453.579987	111.849998	398.279999	...	438.070007	319.609985	336.399994	755.979980	628.299988	444.850006	205.610001	192.809998	486.200012	144.759995
2026-01-05	267.260010	331.559998	1228.189941	56.889999	498.519989	616.099976	275.059998	456.549988	114.070000	408.760010	...	451.670013	322.250000	342.019989	757.419983	632.460022	446.510010	207.509995	194.649994	488.450012	145.820007

Ticker	AAPL	ADBE	ASML	BAC	BRK-B	CAT	CME	CRWD	DIS	GLD	...	TSLA	TSM	UNH	VGT	VOO	VOOG	VOOV	VTV	VUG	VYM
count	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	...	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000	458.000000
mean	222.552679	437.168252	835.958809	43.304069	467.779913	389.081520	236.470775	387.847784	105.330561	277.980262	...	303.332074	201.119318	440.994565	617.778433	536.028584	365.229192	184.176589	169.630523	406.587243	126.395603
std	28.227665	76.543920	137.833934	5.724365	36.503627	78.722380	32.720417	82.221034	10.323902	56.767730	...	94.636566	49.471145	104.222733	81.988133	50.927276	46.526895	10.670231	11.152038	49.141947	9.622135
min	163.664917	312.399994	590.981628	33.286674	396.730011	270.854095	183.360397	217.889999	80.826134	199.710007	...	142.050003	124.747917	234.701340	468.845581	445.162018	283.491333	160.399734	148.670639	319.818878	109.263657
25%	204.992336	361.214996	720.596664	38.356688	445.197495	337.269882	203.922478	320.304993	96.814312	230.795002	...	221.462502	165.846745	331.912491	556.428314	496.731300	331.431778	175.030312	161.032127	370.120850	118.651709
50%	223.477219	438.600006	797.170013	43.228786	472.275009	358.955170	237.254982	377.800003	109.514038	263.349991	...	302.714996	190.106636	473.674393	605.396118	531.847992	360.575485	183.908020	169.886086	403.221786	126.483810
75%	238.251236	502.427505	954.881851	46.758083	496.355011	411.348167	268.550484	461.902496	112.741642	309.809990	...	389.190002	231.559891	530.617584	682.324539	574.526367	405.175117	191.088543	176.452358	448.428337	133.308155
max	286.190002	586.549988	1228.189941	56.889999	539.799988	625.609985	286.685730	557.530029	123.176605	416.739990	...	489.880005	322.250000	607.890625	800.707397	634.840027	454.862305	207.509995	194.649994	503.744995	146.816788

Ticker	AAPL	ADBE	ASML	BAC	BRK-B	CAT	CME	CRWD	DIS	GLD	...	TSLA	TSM	UNH	VGT	VOO	VOOG	VOOV	VTV	VUG	VYM
Date
2025-12-29	0.001317	-0.001809	-0.006292	-0.014599	0.005519	-0.007530	0.006107	-0.010973	0.005548	-0.043528	...	-0.032724	-0.006340	-0.008709	-0.005305	-0.003529	-0.004637	-0.001401	-0.000986	-0.005090	-0.002688
2025-12-30	-0.002484	-0.001841	0.005760	-0.001265	0.005309	-0.002108	-0.009303	-0.000588	0.005254	0.000728	...	-0.011335	-0.004453	0.009789	-0.002896	-0.001391	-0.001850	-0.001065	-0.001142	-0.001726	-0.001037
2025-12-31	-0.004468	-0.007149	-0.002127	-0.005065	-0.002104	-0.007828	-0.009970	-0.014444	-0.008886	-0.006468	...	-0.010365	0.014387	-0.006172	-0.009344	-0.007266	-0.007102	-0.007558	-0.007174	-0.007789	-0.007126
2026-01-02	-0.003127	-0.047687	0.087787	0.017273	-0.011539	0.044583	-0.012451	-0.032383	-0.016876	0.004971	...	-0.025905	0.051729	0.019054	0.002919	0.001866	0.000585	0.003710	0.009529	-0.003403	0.008640
2026-01-05	-0.013837	-0.005220	0.055345	0.016801	0.003361	0.029562	0.019950	0.006548	0.019848	0.026313	...	0.031045	0.008260	0.016706	0.001905	0.006621	0.003732	0.009241	0.009543	0.004628	0.007323

	Mean	Median	Std Dev	Skewness	Kurtosis
Ticker
SERV	0.0061	-0.0034	0.1318	5.9456	90.6288
QYLD	0.0005	0.0006	0.0094	2.0870	42.2459
UNH	-0.0003	0.0006	0.0252	-2.2992	20.7657
VOOV	0.0005	0.0006	0.0086	0.4293	18.1899
VOO	0.0008	0.0011	0.0102	0.6920	17.8392
ADBE	-0.0009	-0.0001	0.0214	-1.1348	15.0829
QQQM	0.0009	0.0016	0.0133	0.7721	14.1523
VTV	0.0006	0.0005	0.0084	-0.0227	14.0826
VUG	0.0009	0.0014	0.0133	0.7047	14.0708
VYM	0.0006	0.0007	0.0086	0.0657	13.7854
AAPL	0.0012	0.0016	0.0180	0.9798	13.5882
DIS	0.0003	-0.0001	0.0171	0.2075	13.1759
SPYG	0.0010	0.0017	0.0132	0.6352	13.0041
VOOG	0.0010	0.0016	0.0132	0.5869	12.9678
LMT	0.0006	0.0009	0.0144	-1.7608	12.1996
VGT	0.0010	0.0022	0.0160	0.5416	11.9247
SOXX	0.0010	0.0012	0.0238	0.4233	9.1252
BAC	0.0012	0.0014	0.0159	-0.6499	8.7776
SPG	0.0007	0.0014	0.0149	-0.5224	8.4795
META	0.0008	0.0009	0.0221	0.2239	8.3067
MSFT	0.0005	0.0011	0.0142	0.5917	8.2515
BRK-B	0.0005	0.0008	0.0107	-0.2961	7.6443
PLTR	0.0049	0.0030	0.0398	0.8398	6.5827
NU	0.0014	0.0019	0.0270	-0.7662	6.5808
ASML	0.0009	0.0021	0.0273	-0.3530	6.3391
CAT	0.0015	0.0009	0.0188	0.5463	6.2754
NFLX	0.0011	0.0015	0.0199	-0.1149	6.2151
SPYD	0.0004	0.0007	0.0090	-0.2973	5.6996
MAR	0.0006	0.0006	0.0163	0.3715	5.2052
CRWD	0.0012	0.0013	0.0288	0.2218	4.8889
NVDA	0.0022	0.0027	0.0319	-0.0131	4.5842
GOOG	0.0020	0.0029	0.0189	0.4433	4.5308
GOOGL	0.0021	0.0029	0.0191	0.4620	4.4202
TSLA	0.0029	0.0010	0.0408	0.6801	4.3883
PBR	0.0002	0.0007	0.0184	-0.2319	3.8937
TSM	0.0021	0.0027	0.0257	-0.0901	3.0021
GLD	0.0016	0.0020	0.0115	-0.5681	2.9093
MCD	0.0002	0.0001	0.0116	-0.1298	2.8369
OMAB	0.0015	0.0011	0.0217	-0.0996	2.6707
KO	0.0005	0.0003	0.0099	0.2686	2.4035
SOFI	0.0036	0.0047	0.0364	-0.0165	2.3834
PSA	0.0000	0.0004	0.0143	-0.2022	1.8007
CME	0.0008	0.0014	0.0110	-0.6110	1.7262
QSR	-0.0001	-0.0004	0.0143	-0.2684	1.4833
HD	0.0000	-0.0010	0.0140	0.0574	1.4739

Ticker	AAPL	ADBE	ASML	BAC	BRK-B	CAT	CME	CRWD	DIS	GLD	...	TSLA	TSM	UNH	VGT	VOO	VOOG	VOOV	VTV	VUG	VYM
Date
2025-12-29	61.654779	-35.985789	8.910560	61.985575	24.283764	75.127795	38.696582	47.409012	5.601360	97.688834	...	162.142138	111.308356	-28.452585	47.946322	37.928557	51.768926	22.272159	27.397622	46.504114	30.200598
2025-12-30	61.253226	-36.103607	9.537870	61.780716	24.943569	74.758547	37.406345	47.322285	6.156230	97.832666	...	159.170755	110.367379	-27.752206	47.517865	37.736686	51.488117	22.141975	27.252092	46.251283	30.065634
2025-12-31	60.532818	-36.560389	9.304925	60.961281	24.680640	73.390473	36.036416	45.194363	5.212944	96.553086	...	156.484550	113.393914	-28.198104	46.139413	36.735916	50.412272	21.218855	26.339234	45.112059	29.138844
2026-01-02	60.030909	-39.585639	18.900502	63.741523	23.241973	81.120654	34.342689	40.492482	3.437356	97.530124	...	149.840320	124.432598	-26.829970	46.565930	36.991012	50.500238	21.668577	27.543152	44.618307	30.254584
2026-01-05	57.816541	-39.901032	25.481102	66.492494	23.656207	86.474885	37.022770	41.412414	5.490383	102.727769	...	157.596682	126.286437	-25.607571	46.845111	37.898043	51.061845	22.792888	28.760304	45.287560	31.208380

	Volatility (%)
Ticker
SERV	209.15
TSLA	64.71
PLTR	63.15
SOFI	57.83
NVDA	50.56
CRWD	45.66
ASML	43.30
NU	42.78
TSM	40.79
UNH	39.95
SOXX	37.78
META	35.01
OMAB	34.40
ADBE	34.03
NFLX	31.53
GOOGL	30.36
GOOG	29.97
CAT	29.92
PBR	29.26
AAPL	28.62
DIS	27.16
MAR	25.89
VGT	25.43
BAC	25.18
SPG	23.62
LMT	22.85
PSA	22.66
QSR	22.63
MSFT	22.50
HD	22.24
QQQM	21.11
VUG	21.04
SPYG	20.98
VOOG	20.90
MCD	18.39
GLD	18.22
CME	17.54
BRK-B	17.04
VOO	16.15
KO	15.68
QYLD	14.87
SPYD	14.34
VYM	13.64
VOOV	13.62
VTV	13.33

	Volatility (%)
count	45.000000
mean	32.794889
std	29.833583
min	13.330000
25%	20.900000
50%	25.430000
75%	35.010000
max	209.150000

	Current QTY	Close Price	Investment	Weights
Ticker
NVDA	100.53	188.12	18911.48	0.27
MSFT	19.75	472.85	9339.75	0.14
AAPL	17.04	267.26	4554.12	0.07
PLTR	21.02	174.04	3658.95	0.05
TSM	10.03	322.25	3233.66	0.05
VOOG	6.30	446.51	2811.39	0.04
GLD	6.45	408.76	2636.24	0.04
TSLA	4.92	451.67	2224.30	0.03
VOOV	10.36	207.51	2150.75	0.03
QYLD	100.86	17.76	1791.28	0.03
UNH	5.04	342.02	1722.64	0.03
QQQM	6.38	254.43	1622.13	0.02
VGT	2.02	757.42	1529.25	0.02
DIS	13.03	114.07	1486.23	0.02
VOO	2.29	632.46	1447.28	0.02
META	1.58	658.79	1039.45	0.02
PBR	87.44	11.74	1026.60	0.01
SOXX	3.05	318.06	968.76	0.01
CRWD	1.46	456.55	664.40	0.01
GOOGL	2.01	316.54	636.79	0.01
CME	2.00	275.06	550.12	0.01
GOOG	1.61	317.32	512.10	0.01
VUG	1.01	488.45	492.50	0.01
KO	6.23	67.94	423.06	0.01
QSR	5.26	66.74	350.73	0.01
ASML	0.26	1228.19	324.45	0.00
MCD	1.05	299.86	315.95	0.00
VYM	2.10	145.82	306.92	0.00
NU	13.62	17.94	244.39	0.00
OMAB	2.16	109.26	236.47	0.00
CAT	0.33	616.10	205.93	0.00
LMT	0.32	511.57	163.25	0.00
SPYD	3.65	43.69	159.31	0.00
SOFI	5.32	29.28	155.76	0.00
ADBE	0.47	331.56	154.20	0.00
MAR	0.46	311.03	142.13	0.00
BRK-B	0.24	498.52	122.03	0.00
NFLX	1.20	91.46	109.50	0.00
SPYG	1.02	107.16	109.19	0.00
BAC	1.47	56.89	83.56	0.00
VTV	0.40	194.65	78.82	0.00
PSA	0.19	260.90	49.62	0.00
SERV	3.36	12.68	42.67	0.00
SPG	0.20	183.11	36.40	0.00
HD	0.02	344.09	7.21	0.00

	EE.UU. (S&P 500)	EE.UU. (NASDAQ)	EE.UU. (DJIA)	EE.UU. (Russell 100)	México (IPC)	Japón (Nikkei 225)	Alemania (DAX)	Reino Unido (FTSE 100)
Date
2025-12-29	48461.929688	9866.500000	24351.119141	6905.740234	23474.349609	65347.078125	50526.921875	3766.949951
2025-12-30	48367.058594	9940.700195	24490.410156	6896.240234	23419.080078	64366.699219	50339.480469	3761.540039
2025-12-31	48063.289062	9931.400391	NaN	6845.500000	23241.990234	64308.289062	NaN	3732.870117
2026-01-02	48382.390625	9951.099609	24539.339844	6858.470215	23235.630859	64141.359375	NaN	3742.669922
2026-01-05	48977.179688	10004.599609	24868.689453	6902.049805	23395.820312	65014.371094	51832.800781	3769.020020

	EE.UU. (S&P 500)	EE.UU. (NASDAQ)	EE.UU. (DJIA)	EE.UU. (Russell 100)	México (IPC)	Japón (Nikkei 225)	Alemania (DAX)	Reino Unido (FTSE 100)
Date
2025-12-18	0.001376	0.006497	0.009971	0.007934	0.013794	0.020459	-0.010316	0.007753
2025-12-19	0.003817	0.006058	0.003674	0.008818	0.013095	0.002502	0.010320	0.008832
2025-12-22	0.004732	-0.003173	-0.000182	0.006436	0.005200	0.012682	0.018082	0.006793
2025-12-23	0.001649	0.002352	0.002310	0.004550	0.005677	0.012616	0.000208	0.003815
2025-12-30	-0.001958	0.007520	0.005720	-0.001376	-0.002354	-0.015003	-0.003710	-0.001436

	EE.UU. (S&P 500)	EE.UU. (NASDAQ)	EE.UU. (DJIA)	EE.UU. (Russell 100)	México (IPC)	Japón (Nikkei 225)	Alemania (DAX)	Reino Unido (FTSE 100)
Date
2025-12-18	13.266130	17.128699	23.859387	22.352113	37.085787	8.299930	14.406983	21.228400
2025-12-19	13.698483	17.838302	24.314403	23.431021	38.880875	8.570921	15.587699	22.299108
2025-12-22	14.236539	17.464450	24.291731	24.225485	39.603123	9.947766	17.677733	23.129847
2025-12-23	14.424869	17.740671	24.578813	24.790759	40.395675	11.334842	17.702203	23.599630
2025-12-30	14.200866	18.626130	25.291417	24.619089	40.065119	9.664525	17.265559	23.422122

	EE.UU. (S&P 500)	EE.UU. (NASDAQ)	EE.UU. (DJIA)	EE.UU. (Russell 100)	México (IPC)	Japón (Nikkei 225)	Alemania (DAX)	Reino Unido (FTSE 100)	Initial Portfolio
Date
2025-12-18	0.001376	0.006497	0.009971	0.007934	0.013794	0.020459	-0.010316	0.007753	0.014941
2025-12-19	0.003817	0.006058	0.003674	0.008818	0.013095	0.002502	0.010320	0.008832	0.017300
2025-12-22	0.004732	-0.003173	-0.000182	0.006436	0.005200	0.012682	0.018082	0.006793	0.007409
2025-12-23	0.001649	0.002352	0.002310	0.004550	0.005677	0.012616	0.000208	0.003815	0.011454
2025-12-30	-0.001958	0.007520	0.005720	-0.001376	-0.002354	-0.015003	-0.003710	-0.001436	-0.002100

	Mean	Median	Std Dev	Skewness	Kurtosis
Ticker
portfolio daily returns	0.0015	0.0025	0.0165	0.5720	10.2486

	Annualized Returns (%)	Annualized Volatility (%)	Sharpe Ratio
Initial Portfolio	48.292658	27.296752	1.617066
México (IPC)	26.592593	22.904904	0.979729
EE.UU. (DJIA)	17.405433	16.435356	0.806398
EE.UU. (Russell 100)	17.143433	17.223190	0.754299
EE.UU. (NASDAQ)	12.847404	11.685753	0.744103
Reino Unido (FTSE 100)	16.486908	17.402071	0.708818
EE.UU. (S&P 500)	10.614472	15.286125	0.422767
Alemania (DAX)	15.081337	27.297079	0.400385
Japón (Nikkei 225)	7.847153	15.980321	0.231231

	Initial_Value_USD	Final_Value_USD	Margin_USD	Yield_Perc
Ticker
PLTR	100,000.00	668,356.33	568,356.33	568.36
SOFI	100,000.00	379,766.54	279,766.54	279.77
TSLA	100,000.00	257,596.68	157,596.68	157.60
GOOGL	100,000.00	235,530.37	135,530.37	135.53
GOOG	100,000.00	234,573.32	134,573.32	134.57
TSM	100,000.00	226,286.44	126,286.44	126.29
NVDA	100,000.00	215,035.17	115,035.17	115.04
GLD	100,000.00	202,727.77	102,727.77	102.73
CAT	100,000.00	186,474.88	86,474.88	86.47
OMAB	100,000.00	179,496.09	79,496.09	79.50
BAC	100,000.00	166,492.49	66,492.49	66.49
NU	100,000.00	162,059.63	62,059.63	62.06
AAPL	100,000.00	157,816.54	57,816.54	57.82
NFLX	100,000.00	151,218.55	51,218.55	51.22
SPYG	100,000.00	151,135.28	51,135.28	51.14
VOOG	100,000.00	151,061.84	51,061.84	51.06
VGT	100,000.00	146,845.11	46,845.11	46.85
VUG	100,000.00	145,287.56	45,287.56	45.29
QQQM	100,000.00	142,482.23	42,482.23	42.48
CRWD	100,000.00	141,412.41	41,412.41	41.41
SOXX	100,000.00	141,291.84	41,291.84	41.29
VOO	100,000.00	137,898.04	37,898.04	37.90
CME	100,000.00	137,022.77	37,022.77	37.02
SPG	100,000.00	132,267.28	32,267.28	32.27
VYM	100,000.00	131,208.38	31,208.38	31.21
META	100,000.00	130,979.08	30,979.08	30.98
VTV	100,000.00	128,760.30	28,760.30	28.76
MAR	100,000.00	126,464.20	26,464.20	26.46
ASML	100,000.00	125,481.10	25,481.10	25.48
QYLD	100,000.00	124,332.93	24,332.93	24.33
LMT	100,000.00	123,931.91	23,931.91	23.93
BRK-B	100,000.00	123,656.21	23,656.21	23.66
VOOV	100,000.00	122,792.89	22,792.89	22.79
KO	100,000.00	121,103.27	21,103.27	21.10
SPYD	100,000.00	119,553.37	19,553.37	19.55
MSFT	100,000.00	117,919.92	17,919.92	17.92
MCD	100,000.00	106,823.52	6,823.52	6.82
DIS	100,000.00	105,490.38	5,490.38	5.49
PBR	100,000.00	103,522.28	3,522.28	3.52
PSA	100,000.00	96,453.83	-3,546.17	-3.55
HD	100,000.00	96,198.91	-3,801.09	-3.80
QSR	100,000.00	89,795.70	-10,204.30	-10.20
UNH	100,000.00	74,392.43	-25,607.57	-25.61
ADBE	100,000.00	60,098.97	-39,901.03	-39.90
SERV	100,000.00	53,277.31	-46,722.69	-46.72

	Total_Invested	Final_Value_USD	Margin_USD	Yield_Perc_%
Ticker
PLTR	2,300.00	7,347.91	5,047.91	219.47
SOFI	2,300.00	5,477.07	3,177.07	138.13
SERV	2,300.00	4,211.12	1,911.12	83.09
GOOGL	2,300.00	3,851.00	1,551.00	67.43
TSM	2,300.00	3,837.66	1,537.66	66.85
GOOG	2,300.00	3,831.20	1,531.20	66.57
CAT	2,300.00	3,662.59	1,362.59	59.24
TSLA	2,300.00	3,647.39	1,347.39	58.58
GLD	2,300.00	3,449.93	1,149.93	50.00
ASML	2,300.00	3,437.25	1,137.25	49.45
NVDA	2,300.00	3,268.31	968.31	42.10
OMAB	2,300.00	3,194.73	894.73	38.90
SOXX	2,300.00	3,152.16	852.16	37.05
NU	2,300.00	3,143.96	843.96	36.69
BAC	2,300.00	2,997.71	697.71	30.34
VGT	2,300.00	2,833.99	533.99	23.22
CRWD	2,300.00	2,833.33	533.33	23.19
VOOG	2,300.00	2,820.17	520.17	22.62
SPYG	2,300.00	2,820.01	520.01	22.61
QQQM	2,300.00	2,775.69	475.69	20.68
VUG	2,300.00	2,769.29	469.29	20.40
MAR	2,300.00	2,760.62	460.62	20.03
AAPL	2,300.00	2,754.08	454.08	19.74
VOO	2,300.00	2,703.26	403.26	17.53
CME	2,300.00	2,692.53	392.53	17.07
VYM	2,300.00	2,632.25	332.25	14.45
SPG	2,300.00	2,629.02	329.02	14.31
VTV	2,300.00	2,616.17	316.17	13.75
QYLD	2,300.00	2,601.34	301.34	13.10
META	2,300.00	2,578.94	278.94	12.13
VOOV	2,300.00	2,568.62	268.62	11.68
LMT	2,300.00	2,491.15	191.15	8.31
MSFT	2,300.00	2,480.81	180.81	7.86
DIS	2,300.00	2,471.46	171.46	7.45
BRK-B	2,300.00	2,434.09	134.09	5.83
NFLX	2,300.00	2,431.42	131.42	5.71
SPYD	2,300.00	2,426.08	126.08	5.48
MCD	2,300.00	2,402.63	102.63	4.46
KO	2,300.00	2,378.96	78.96	3.43
QSR	2,300.00	2,325.70	25.70	1.12
PBR	2,300.00	2,209.65	-90.35	-3.93
HD	2,300.00	2,166.31	-133.69	-5.81
PSA	2,300.00	2,114.69	-185.31	-8.06
UNH	2,300.00	1,932.03	-367.97	-16.00
ADBE	2,300.00	1,828.83	-471.17	-20.49

	Correlation (r)	R-Squared	p-value	Slope (Beta)	Std Error	Intercept (Alpha)	Vol_GOOGL	Vol_GOOG	Significant?	Vol_VOOG	Vol_SPYG	Vol_VUG	Vol_QQQM	Vol_VYM	Vol_VTV	Vol_VGT	Vol_CME	Vol_ASML	Vol_NVDA	Vol_KO	Vol_TSM	Vol_SOXX
Pair
GOOGL vs GOOG	0.9979	0.9958	0.0000	0.9850	0.0030	0.0000	0.0191	0.0189	Yes	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
VOOG vs SPYG	0.9974	0.9948	0.0000	1.0013	0.0034	0.0000	nan	nan	Yes	0.0132	0.0132	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
VUG vs VOOG	0.9910	0.9821	0.0000	0.9841	0.0062	0.0001	nan	nan	Yes	0.0132	nan	0.0132	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
VUG vs SPYG	0.9884	0.9770	0.0000	0.9854	0.0071	0.0001	nan	nan	Yes	nan	0.0132	0.0132	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
SPYG vs QQQM	0.9866	0.9733	0.0000	0.9926	0.0077	-0.0001	nan	nan	Yes	nan	0.0132	nan	0.0133	nan	nan	nan	nan	nan	nan	nan	nan	nan
VUG vs QQQM	0.9863	0.9729	0.0000	0.9894	0.0077	-0.0000	nan	nan	Yes	nan	nan	0.0132	0.0133	nan	nan	nan	nan	nan	nan	nan	nan	nan
VOOG vs QQQM	0.9862	0.9725	0.0000	0.9962	0.0078	-0.0001	nan	nan	Yes	0.0132	nan	nan	0.0133	nan	nan	nan	nan	nan	nan	nan	nan	nan
VYM vs VTV	0.9839	0.9680	0.0000	0.9611	0.0082	-0.0000	nan	nan	Yes	nan	nan	nan	nan	0.0086	0.0084	nan	nan	nan	nan	nan	nan	nan
VGT vs QQQM	0.9759	0.9524	0.0000	0.8100	0.0085	0.0001	nan	nan	Yes	nan	nan	nan	0.0133	nan	nan	0.0160	nan	nan	nan	nan	nan	nan
VGT vs SPYG	0.9720	0.9449	0.0000	0.8019	0.0091	0.0002	nan	nan	Yes	nan	0.0132	nan	nan	nan	nan	0.0160	nan	nan	nan	nan	nan	nan
CME vs ASML	-0.2532	0.0641	0.0000	-0.6253	0.1120	0.0013	nan	nan	Yes	nan	nan	nan	nan	nan	nan	nan	0.0110	0.0272	nan	nan	nan	nan
NVDA vs KO	-0.2519	0.0634	0.0000	-0.0781	0.0141	0.0006	nan	nan	Yes	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0318	0.0099	nan	nan
TSM vs KO	-0.2442	0.0596	0.0000	-0.0939	0.0175	0.0007	nan	nan	Yes	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0099	0.0257	nan
SOXX vs CME	-0.2406	0.0579	0.0000	-0.1117	0.0211	0.0009	nan	nan	Yes	nan	nan	nan	nan	nan	nan	nan	0.0110	nan	nan	nan	nan	0.0238
TSM vs CME	-0.2195	0.0482	0.0000	-0.0944	0.0197	0.0010	nan	nan	Yes	nan	nan	nan	nan	nan	nan	nan	0.0110	nan	nan	nan	0.0257	nan
VGT vs CME	-0.1855	0.0344	0.0001	-0.1279	0.0318	0.0009	nan	nan	Yes	nan	nan	nan	nan	nan	nan	0.0160	0.0110	nan	nan	nan	nan	nan
NVDA vs CME	-0.1810	0.0328	0.0001	-0.0628	0.0160	0.0009	nan	nan	Yes	nan	nan	nan	nan	nan	nan	nan	0.0110	nan	0.0318	nan	nan	nan
SOXX vs KO	-0.1750	0.0306	0.0002	-0.0726	0.0192	0.0005	nan	nan	Yes	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0099	nan	0.0238
GOOGL vs CME	-0.1610	0.0259	0.0005	-0.0930	0.0267	0.0009	0.0191	nan	Yes	nan	nan	nan	nan	nan	nan	nan	0.0110	nan	nan	nan	nan	nan
GOOG vs CME	-0.1578	0.0249	0.0007	-0.0924	0.0271	0.0009	nan	0.0189	Yes	nan	nan	nan	nan	nan	nan	nan	0.0110	nan	nan	nan	nan	nan

	Correlation (r)	R-Squared	p-value	Slope (Beta)	Std Error	Intercept (Alpha)	Vol_Reino Unido (FTSE 100)	Vol_EE.UU. (Russell 100)	Significant?	Vol_México (IPC)	Vol_Initial Portfolio	Vol_EE.UU. (S&P 500)	Vol_EE.UU. (DJIA)	Vol_EE.UU. (NASDAQ)
Pair
Reino Unido (FTSE 100) vs EE.UU. (Russell 100)	0.9990	0.9981	0.0000	0.9888	0.0023	0.0000	0.0109	0.0108	Yes	nan	nan	nan	nan	nan
México (IPC) vs EE.UU. (Russell 100)	0.9685	0.9381	0.0000	0.7283	0.0100	-0.0001	nan	0.0108	Yes	0.0144	nan	nan	nan	nan
Reino Unido (FTSE 100) vs México (IPC)	0.9656	0.9323	0.0000	1.2709	0.0182	0.0002	0.0109	nan	Yes	0.0144	nan	nan	nan	nan
Initial Portfolio vs México (IPC)	0.9473	0.8974	0.0000	0.7949	0.0143	-0.0005	nan	nan	Yes	0.0144	0.0172	nan	nan	nan
Reino Unido (FTSE 100) vs EE.UU. (S&P 500)	0.9064	0.8216	0.0000	0.7962	0.0198	-0.0001	0.0109	nan	Yes	nan	nan	0.0096	nan	nan
EE.UU. (Russell 100) vs EE.UU. (S&P 500)	0.9004	0.8107	0.0000	0.7991	0.0206	-0.0001	nan	0.0108	Yes	nan	nan	0.0096	nan	nan
Initial Portfolio vs EE.UU. (Russell 100)	0.8918	0.7953	0.0000	0.5627	0.0152	-0.0004	nan	0.0108	Yes	nan	0.0172	nan	nan	nan
Initial Portfolio vs Reino Unido (FTSE 100)	0.8840	0.7815	0.0000	0.5636	0.0159	-0.0004	0.0109	nan	Yes	nan	0.0172	nan	nan	nan
México (IPC) vs EE.UU. (S&P 500)	0.7916	0.6266	0.0000	0.5283	0.0217	-0.0001	nan	nan	Yes	0.0144	nan	0.0096	nan	nan
EE.UU. (DJIA) vs EE.UU. (NASDAQ)	0.7148	0.5110	0.0000	0.5083	0.0265	0.0002	nan	nan	Yes	nan	nan	nan	0.0103	0.0074

	Current QTY	Close Price	Investment	Weights	Optimal Weights	New Investment usd	New QTY	Position_to_Optimal
Ticker
AAPL	17.040	267.26	4554.121	0.066	0.000	0.000	0.000	-17.040
ADBE	0.465	331.56	154.205	0.002	0.000	0.000	0.000	-0.465
ASML	0.264	1228.19	324.451	0.005	0.000	0.000	0.000	-0.264
BAC	1.469	56.89	83.563	0.001	0.014	998.060	17.544	16.075
BRK-B	0.245	498.52	122.028	0.002	0.000	0.000	0.000	-0.245
CAT	0.334	616.10	205.932	0.003	0.026	1755.209	2.849	2.515
CME	2.000	275.06	550.120	0.008	0.191	13174.395	47.896	45.896
CRWD	1.455	456.55	664.399	0.010	0.000	0.000	0.000	-1.455
DIS	13.029	114.07	1486.232	0.022	0.000	0.000	0.000	-13.029
GLD	6.449	408.76	2636.236	0.038	0.398	27429.449	67.104	60.655
GOOG	1.614	317.32	512.100	0.007	0.000	0.000	0.000	-1.614
GOOGL	2.012	316.54	636.790	0.009	0.117	8046.430	25.420	23.408
HD	0.021	344.09	7.210	0.000	0.000	0.000	0.000	-0.021
KO	6.227	67.94	423.063	0.006	0.121	8335.524	122.689	116.462
LMT	0.319	511.57	163.252	0.002	0.001	68.832	0.135	-0.185
MAR	0.457	311.03	142.130	0.002	0.000	0.000	0.000	-0.457
MCD	1.054	299.86	315.954	0.005	0.000	0.000	0.000	-1.054
META	1.578	658.79	1039.452	0.015	0.000	0.000	0.000	-1.578
MSFT	19.752	472.85	9339.751	0.136	0.000	0.000	0.000	-19.752
NFLX	1.197	91.46	109.498	0.002	0.000	0.000	0.000	-1.197
NU	13.623	17.94	244.388	0.004	0.000	0.000	0.000	-13.623
NVDA	100.529	188.12	18911.482	0.275	0.000	0.000	0.000	-100.529
OMAB	2.164	109.26	236.470	0.003	0.015	1032.476	9.450	7.285
PBR	87.444	11.74	1026.597	0.015	0.000	0.000	0.000	-87.444
PLTR	21.024	174.04	3658.947	0.053	0.085	5830.048	33.498	12.475
PSA	0.190	260.90	49.624	0.001	0.000	0.000	0.000	-0.190
QQQM	6.376	254.43	1622.126	0.024	0.000	0.000	0.000	-6.376
QSR	5.255	66.74	350.734	0.005	0.000	0.000	0.000	-5.255
QYLD	100.860	17.76	1791.281	0.026	0.000	0.000	0.000	-100.860
SERV	3.365	12.68	42.666	0.001	0.008	536.888	42.341	38.976
SOFI	5.320	29.28	155.765	0.002	0.012	846.630	28.915	23.595
SOXX	3.046	318.06	968.755	0.014	0.000	0.000	0.000	-3.046
SPG	0.199	183.11	36.404	0.001	0.000	0.000	0.000	-0.199
SPYD	3.646	43.69	159.305	0.002	0.000	0.000	0.000	-3.646
SPYG	1.019	107.16	109.187	0.002	0.000	0.000	0.000	-1.019
TSLA	4.925	451.67	2224.304	0.032	0.000	0.000	0.000	-4.925
TSM	10.035	322.25	3233.660	0.047	0.011	777.799	2.414	-7.621
UNH	5.037	342.02	1722.644	0.025	0.000	0.000	0.000	-5.037
VGT	2.019	757.42	1529.254	0.022	0.000	0.000	0.000	-2.019
VOO	2.288	632.46	1447.284	0.021	0.000	0.000	0.000	-2.288
VOOG	6.296	446.51	2811.387	0.041	0.000	0.000	0.000	-6.296
VOOV	10.365	207.51	2150.748	0.031	0.000	0.000	0.000	-10.365
VTV	0.405	194.65	78.817	0.001	0.000	0.000	0.000	-0.405
VUG	1.008	488.45	492.502	0.007	0.000	0.000	0.000	-1.008
VYM	2.105	145.82	306.923	0.004	0.000	0.000	0.000	-2.105

Métrica	Valor Óptimo / Bueno	Interpretación
Alpha (α)	> 0 (ideal: +2% a +10% anual)	Exceso de retorno sobre lo esperado por el CAPM; positivo indica valor agregado.
Beta (β)	≈ 1 (mercado), <1 defensivo, >1 agresivo	Sensibilidad al mercado; >1 = más volátil, <1 = más estable.
R² (correlación)	> 0.8 (80% o más)	El portafolio se mueve casi igual que el benchmark (muy “indexado”).
R² (correlación)	0.6 – 0.8	Buena relación con el mercado, pero con diferencias notables.
R² (correlación)	< 0.30	El portafolio se comporta muy distinto al mercado (alta independencia).
µ (Retorno anual)	> 8% estable, > 15% agresivo	Rendimiento esperado anualizado del portafolio.
σ (Volatilidad anual)	10%–20% moderado, <10% defensivo, >25% muy riesgoso	Mide el riesgo total (desviación estándar).
Sharpe Ratio	> 1 bueno, > 1.5 muy bueno, > 2 excelente	Retorno ajustado por riesgo total.
Sortino Ratio	> 2 excelente	Retorno ajustado por riesgo a la baja (mejor si >> Sharpe).
VaR (95%, 1d)	< 2%	Pérdida máxima esperada en un día con 95% de confianza.
CVaR (95%, 1d)	< 3%	Pérdida promedio en los peores días (cola izquierda de la distribución).

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